# https://github.com/njanakiev/folderstats
# https://janakiev.com/blog/python-filesystem-analysis/
# https://networkx.org/documentation/stable/
from google.colab import drive
drive.mount('/content/drive')
%cd /content/drive/My Drive/IDPCode/
Mounted at /content/drive /content/drive/.shortcut-targets-by-id/1d2RlPHkcJvE2Q0NDVZjeNW8VcVz3h_WL/IDPCode
!pip install xlrd
!pip install spacy
!pip install gensim
!pip install ipython-autotime
!pip install pickle5
!pip install textacy
!pip install spacy-langdetect
!pip install pandarallel
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import pandas as pd
import numpy as np
import pickle5 as pickle
import gensim
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# show execution time of cells
%load_ext autotime
# CHANGE PATH FOR THE DATASET FOLDER
DATA_DIRECTORY='/Users/farukcankaya/FC/GoogleDrive/AllPapers'
time: 375 µs
# Utility functions
def remove_dollar_sign(text):
'''
While drawing title count distribution, mathplotlib throws an error.
Because some titles have dollar sign('$'). This method is used to clear the sign.
'''
text = str(text).replace('$', '\$')
return text
import re
import string
import textacy
from textacy import preprocessing
def clean_text(text):
'''
Eliminates links, non alphanumerics, and punctuation.
Returns lower case text.
'''
# Convert to string
text = str(text)
# Remove non-ascii
text = text.encode('ascii', 'ignore').decode('ascii')
# Remove links
text = re.sub('(?:(?:https?|ftp):\/\/)?[\w/\-?=%.]+\.[\w/\-?=%.]+', '', text)
# Remove E-Mail addresses
text = re.sub(r'\b[a-z]+@[a-z]+\b', r'', str(text))
# Remove non-alphanumerics
text = re.sub('\w*\d\w*', ' ', text)
# Remove punctuation and lowercase
text = re.sub('[%s]' % re.escape(string.punctuation), ' ', text.lower())
# Remove newline characters
text = re.sub("\n", r' ', text)
# Remove text in square brackets
text = re.sub(r'\[.*?\]', '', text)
# Remove words containing numbers
text = re.sub(r'\w*\d\w*', '', text)
# https://textacy.readthedocs.io/en/0.10.1/_modules/textacy/preprocessing/normalize.html
text = textacy.preprocessing.normalize.normalize_whitespace(str(text))
# replace words with less than 2 characters
text = re.sub(r'\b[a-z]{1,2}\b', r'', str(text))
return text
import spacy
nlp = spacy.load('en')
def lemmatizer(text, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):
sent = []
doc = nlp(text)
# for word in doc:
# sent.append(word.lemma_)
sent = [token.lemma_ if token.lemma_ not in ['-PRON-'] else '' for token in doc if token.pos_ in allowed_postags]
return " ".join(sent)
# Lemmatization, remove pronouns.
def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):
"""https://spacy.io/api/annotation"""
texts_out = []
for sent in texts:
texts_out.append(lemmatizer(" ".join(sent), allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']))
return texts_out
def remove_stopwords(text):
clean_text = []
for word in text.split(' '):
if word not in stop_list and (len(word) > 2):
clean_text.append(word)
return ' '.join(clean_text)
def display_topics(model, feature_names, no_top_words, no_top_topics, topic_names=None):
count = 0
for ix, topic in enumerate(model.components_):
if count == no_top_topics:
break
if not topic_names or not topic_names[ix]:
print("\nTopic ", (ix + 1))
else:
print("\nTopic: '",topic_names[ix],"'")
print(", ".join([feature_names[i] for i in topic.argsort()[:-no_top_words-1:-1]]))
count += 1
def word_count(text):
return sum([i.strip(string.punctuation).isalpha() for i in text.split()])
from IPython.core.display import display, HTML
def print_html(data):
display(HTML('<div style="width: 90%">'+data+'</div>'))
def print_lemmatized(text, colorized_ignored=False, ignore_type='allowed'):
allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']
doc_lemma = nlp(text)
lemms = []
poses = []
#colors = ['#f88f88','#DA553F','#045E5B','#731425','#F61E9E','#f44f44','#ff0','#0ff','#f0f']
import random
r = lambda: random.randint(0,255)
colors = ['#%02X%02X%02X' % (r(),r(),r()) for i in range(30)]
for token in doc_lemma:
if token.pos_ not in poses:
poses.append(token.pos_)
print(poses)
for token in doc_lemma:
if (ignore_type=='allowed' and token.pos_ not in allowed_postags) or (ignore_type=='stop_word' and token.is_stop):
ignored_color = '#FF8983' if colorized_ignored else colors[poses.index(token.pos_)]
lemms.append('<span style="color: #fff; padding: 0 10px; background-color: '+ignored_color+'"> '+token.lemma_+' </span>')
else:
lemms.append(token.lemma_)
print_html('allowed_postags: '+' '.join(['<span style="color: #fff; padding: 0 10px; background-color: '+colors[allowed_postags.index(i)]+'"> '+i+' </span>' for i in allowed_postags]))
print_html('available: '+' '.join(['<span style="color: #fff; padding: 0 10px; background-color: '+colors[poses.index(i)]+'"> '+i+' </span>' for i in poses]))
print_html(' '.join(lemms))
import os
def get_file_name(file_path):
f = os.path.basename(file_path)
return f.replace('.pdf', '')
def run_in_parallel(dt, function, n_cores=8):
df_n = np.array_split(df, n_cores)
pool = Pool(n_cores)
df = pd.concat(pool.map(func, df_n))
pool.close()
pool.join()
return df
time: 1.74 s
The dataset in 'All_Papers_In_Plain_Text_TIKA.pkl' contains extracted text page by page per document. Each document has one row and has columns as much as they need in the dataset. Any datapreprocessing step is not applied to data in 'All_Papers_In_Plain_Text_TIKA.pkl'. It is not even normalized. It has many documents that have just one page. Our purpose here was to test the accuracy value against different datapreprocessing steps as simple as we can. Afterwards, we will do further tests by normalizing the data. So to summerize:
# dt = pd.read_pickle('en_cleaned_mallet_stop_words_lemmatized_all_pages_separetely.pkl') # https://drive.google.com/file/d/1wOnaUYVSu-vI0tQr9xe_gjCLKcapX1bd/view?usp=sharing
dt = pickle.load(open('en_cleaned_mallet_stop_words_lemmatized_all_pages_separetely.pkl', "rb"))
time: 32 s
# Import
from pandarallel import pandarallel
# Initialization
pandarallel.initialize()
INFO: Pandarallel will run on 8 workers. INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers. time: 49.9 ms
# dt = Dataset Tika
dt = pd.read_pickle('All_Papers_In_Plain_Text_TIKA.pkl') # https://drive.google.com/file/d/1mF88eFEKtni3Sr8jYo2Yo6-rqIunbMgi/view?usp=sharing
time: 7.43 s
col_page = [col for col in dt if col.startswith('page') and not col.endswith('_wc')]
col_page_wc = [col for col in dt if col.startswith('page') and col.endswith('_wc')]
time: 1.33 ms
for page in col_page:
text_series = dt[page].parallel_apply(clean_text)
dt['cleaned_' + page] = text_series
dt['cleaned_' + page + '_wc'] = text_series.parallel_apply(word_count)
time: 8min 18s
def remove_non_english_sent(text):
doc = nlp(text)
return ' '.join([sent.text for sent in doc.sents if sent._.language["language"] == 'en'])
def document_lang(text):
doc = nlp(text)
return doc._.language["language"]
time: 767 µs
import en_core_web_sm
nlp = en_core_web_sm.load()
#https://pypi.org/project/spacy-langdetect/
from spacy_langdetect import LanguageDetector
nlp.add_pipe(LanguageDetector(), name="language_detector", last=True)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 1.74 s
for page in col_page:
dt['en_cleaned_' + page] = dt['cleaned_' + page].parallel_apply(remove_non_english_sent)
time: 13h 20min 54s
dt.to_pickle('en_clean_content_separately.pkl')
time: 52.4 s
stop_words_data1 = open("data/StopWords/StopWords_Auditor.txt", "r").read().lower().split()
stop_words_data2 = open("data/StopWords/StopWords_Currencies.txt", "r").read().lower().split()
stop_words_data3 = open("data/StopWords/StopWords_DatesandNumbers.txt").read().lower().split()
stop_words_data4 = open("data/StopWords/StopWords_Generic.txt").read().lower().split()
stop_words_data5 = open("data/StopWords/StopWords_GenericLong.txt").read().lower().split()
stop_words_data6 = open("data/StopWords/StopWords_Geographic.txt").read().lower().split()
stop_words_data7 = open("data/StopWords/StopWords_Names.txt").read().lower().split()
time: 7.88 ms
mallet_stop_words_en = open("data/StopWords/MALLET_en.txt", "r").read().lower().split()
mallet_stop_words_fr = open("data/StopWords/MALLET_fr.txt", "r").read().lower().split()
time: 2.2 ms
print(f'{len(set(stop_words_data1))} => StopWords_Auditor.txt')
print(f'{len(set(stop_words_data2))} => StopWords_Currencies.txt')
print(f'{len(set(stop_words_data3))} => StopWords_DatesandNumbers.txt')
print(f'{len(set(stop_words_data4))} => StopWords_Generic.txt')
print(f'{len(set(stop_words_data5))} => StopWords_GenericLong.txt')
print(f'{len(set(stop_words_data6))} => StopWords_Geographic.txt')
print(f'{len(set(stop_words_data7))} => StopWords_Names.txt')
print(f'{len(set(mallet_stop_words_en))} => MALLET English Stopwords')
print(f'{len(set(mallet_stop_words_fr))} => MALLET French Stopwords')
8 => StopWords_Auditor.txt 190 => StopWords_Currencies.txt 116 => StopWords_DatesandNumbers.txt 121 => StopWords_Generic.txt 570 => StopWords_GenericLong.txt 199 => StopWords_Geographic.txt 11905 => StopWords_Names.txt 523 => MALLET English Stopwords 126 => MALLET French Stopwords time: 4.62 ms
print(len(stop_words_data1) + \
len(stop_words_data2) + \
len(stop_words_data3) + \
len(stop_words_data4) + \
len(stop_words_data5) + \
len(stop_words_data6) + \
len(stop_words_data7))
print(len(mallet_stop_words_en))
print(len(mallet_stop_words_fr))
14336 524 126 time: 608 µs
stop_words_data2 = list(filter(lambda a : a != "|", stop_words_data2))
stop_words_data3 = list(filter(lambda a : a != "|", stop_words_data3))
stop_words_data6 = list(filter(lambda a : a != "|", stop_words_data6))
stop_words_data7 = list(filter(lambda a : a != "|", stop_words_data7))
time: 2.4 ms
stop_words_data = stop_words_data1 + \
stop_words_data2 + \
stop_words_data3 + \
stop_words_data4 + \
stop_words_data5 + \
stop_words_data6 + \
stop_words_data7 + \
mallet_stop_words_en + \
mallet_stop_words_fr
time: 1.18 ms
# TODO:!!!!
# Find accounting related stop words
#LIST_OF_ACCOUNTING_RELATED_STOP_WORDS = ['accounting','firms','research','audit','financial','paper','information','les','des','que','dans','sur','par','qui','est','aux','une']
LIST_OF_ACCOUNTING_RELATED_STOP_WORDS = []
LIST_OF_ACCOUNTING_RELATED_STOP_WORDS = LIST_OF_ACCOUNTING_RELATED_STOP_WORDS + stop_words_data
time: 546 µs
from gensim.parsing.preprocessing import STOPWORDS
stop_list = STOPWORDS.union(set(LIST_OF_ACCOUNTING_RELATED_STOP_WORDS))
time: 2.14 ms
mallet_fr = pd.DataFrame(data=mallet_stop_words_fr, columns=['Stop Word'])
mallet_fr.to_excel('mallet_french_stopwords.xlsx', engine='xlsxwriter')
# https://drive.google.com/file/d/1JMpcfriApigcV54q4ZPOE7GX8iwNgGBI/view?usp=sharing
mallet_en = pd.DataFrame(data=mallet_stop_words_en, columns=['Stop Word'])
mallet_en.to_excel('mallet_english_stopwords.xlsx', engine='xlsxwriter')
# https://drive.google.com/file/d/1op134plHcerwUds8E44Lw9gsI316Pus3/view?usp=sharing
time: 244 ms
s = ['accounting','firms','research','audit','financial','paper','information','les','des','que','dans','sur','par','qui','est','aux','une']
ss1x = pd.DataFrame(data=s, columns=['Stop Word'])
ss1x.to_excel('accounting_stopwords.xlsx', engine='xlsxwriter')
# https://drive.google.com/file/d/1gZfPtIQyQ82u12ScEvEndMMujfrnh8Gd/view?usp=sharing
ss1x = pd.DataFrame(data=stop_list, columns=['Stop Word'])
ss1x.to_excel('ALL_COMBINED_STOP_WORDS.xlsx', engine='xlsxwriter')
ss2 = pd.DataFrame(data=set(LIST_OF_ACCOUNTING_RELATED_STOP_WORDS), columns=['Stop Word'])
ss2.to_excel('accounting_stopwords.xlsx', engine='xlsxwriter')
for page in col_page:
dt['en_cleaned_stop_words_' + page] = dt['en_cleaned_' + page].parallel_apply(remove_stopwords) # uses 'stop_list' variable
time: 1min 17s
for page in col_page:
dt['en_cleaned_stop_words_lemmatized_' + page] = dt['en_cleaned_stop_words_' + page].parallel_apply(lemmatizer)
time: 1h 15min 50s
dt.to_pickle('en_cleaned_mallet_stop_words_lemmatized_all_pages_separetely.pkl')
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 2min 9s
May lead to confusion, think about that again!!!
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()
def stem_text(text):
word_list = []
for word in text.split(' '):
word_list.append(stemmer.stem(word))
return ' '.join(word_list)
for page in col_page:
dt['cleaned_stop_words_lemmatized_stemmed_' + page] = dt['cleaned_stop_words_lemmatized_' + page].apply(stem_text)
dt.to_pickle('cleaned_stop_words_lemmatized_stemmed_all_pages_separetely.pkl')
text = dt['cleaned_stop_words_page_1'].head(1)[0]
lemmatized_text = dt['cleaned_stop_words_lemmatized_page_1'].head(1)[0].split()
stemmed_text = dt['cleaned_stop_words_lemmatized_stemmed_page_1'].head(1)[0].split()
stemm_words = []
for i in range(len(stemmed_text)):
if stemmed_text[i] == lemmatized_text[i]:
stemm_words.append(stemmed_text[i])
else:
stemm_words.append(stemmed_text[i]+'<span style="background-color: #FF8983">'+(lemmatized_text[i])[len(stemmed_text[i]):]+'</span>')
print_html(' '.join(stemm_words))
print_lemmatized(' '.join(LIST_OF_ACCOUNTING_RELATED_STOP_WORDS), colorized_ignored=False, ignore_type='stop_word')
print_lemmatized(dt['en_cleaned_page_1'].head(1)[0])
print_lemmatized(dt['en_cleaned_stop_words_page_1'].head(1)[0])
print_lemmatized(dt['en_cleaned_stop_words_lemmatized_page_1'].head(1)[0])
dt['content_3'] = dt['en_cleaned_stop_words_lemmatized_page_1'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_2'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_3'].apply(str)
dt['content_5'] = dt['en_cleaned_stop_words_lemmatized_page_1'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_2'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_3'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_4'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_5'].apply(str)
dt['content_10'] = dt['en_cleaned_stop_words_lemmatized_page_1'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_2'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_3'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_4'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_5'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_6'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_7'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_8'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_9'].apply(str) + ' ' \
+ dt['en_cleaned_stop_words_lemmatized_page_10'].apply(str)
time: 1.52 s
dt3c = pd.DataFrame(dt, columns=['file_path', 'Author', 'date', 'title', 'content_3', 'content_5', 'content_10'])
time: 20.1 ms
dt3c['content_3_lang'] = dt3c['content_3'].parallel_apply(document_lang)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 9min 50s
dt3c['content_5_lang'] = dt3c['content_5'].parallel_apply(document_lang)
dt3c['content_10_lang'] = dt3c['content_10'].parallel_apply(document_lang)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 43min 58s
dt3c.info()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15751 entries, 0 to 15750 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15751 non-null object 1 Author 7830 non-null object 2 date 15525 non-null object 3 title 14074 non-null object 4 content_3 15751 non-null object 5 content_5 15751 non-null object 6 content_10 15751 non-null object 7 content_3_lang 15751 non-null object dtypes: object(8) memory usage: 984.6+ KB time: 843 ms
print(dt3c['content_3_lang'].unique())
for lang in dt3c['content_3_lang'].unique():
print(f'{lang}: {list(dt3c["content_3_lang"].values).count(lang)}')
['en' 'UNKNOWN' 'sq' 'it' 'ro' 'nl' 'pt' 'fr' 'da'] en: 15190 UNKNOWN: 532 sq: 6 it: 14 ro: 4 nl: 1 pt: 1 fr: 2 da: 1 time: 15.3 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c_copy = dt3c.copy()
for lang in dt3c['content_3_lang'].unique():
if lang != 'en':
unknown_lang_indexes_content_3 = dt3c.index[dt3c['content_3_lang'].str.match(lang)]
for lang in dt3c['content_10_lang'].unique():
if lang != 'en':
unknown_lang_indexes_content_10 = dt3c.index[dt3c['content_10_lang'].str.match(lang)]
dt3c = dt3c.drop(unknown_lang_indexes_content_3)
dt3c = dt3c.drop(unknown_lang_indexes_content10)
dt3c.info()
dt3c.reset_index(drop=True, inplace=True)
dt3c.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 15189 entries, 0 to 15218 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 Author 7794 non-null object 2 date 14976 non-null object 3 title 13543 non-null object 4 content_3 15189 non-null object 5 content_5 15189 non-null object 6 content_10 15189 non-null object 7 content_3_lang 15189 non-null object 8 content_5_lang 15189 non-null object 9 content_10_lang 15189 non-null object dtypes: object(10) memory usage: 1.3+ MB <class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 Author 7794 non-null object 2 date 14976 non-null object 3 title 13543 non-null object 4 content_3 15189 non-null object 5 content_5 15189 non-null object 6 content_10 15189 non-null object 7 content_3_lang 15189 non-null object 8 content_5_lang 15189 non-null object 9 content_10_lang 15189 non-null object dtypes: object(10) memory usage: 1.2+ MB time: 295 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c_copy2 = dt3c.copy()
non_matched_indexes = []
for i in range(0, len(dt3c['file_path'])):
if not rt['Filename'].str.match(get_file_name(dt3c['file_path'][i])).any():
non_matched_indexes.append(i)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 1min 43s
print(len(non_matched_indexes))
4205 time: 374 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
print(non_matched_indexes)
[1, 3, 11, 13, 14, 17, 18, 19, 20, 25, 26, 30, 33, 37, 41, 42, 43, 45, 46, 47, 49, 50, 51, 52, 53, 57, 60, 61, 64, 70, 71, 72, 73, 74, 75, 81, 96, 99, 102, 103, 106, 110, 112, 116, 117, 119, 121, 122, 123, 124, 125, 126, 135, 137, 138, 142, 144, 146, 148, 149, 151, 156, 159, 161, 162, 163, 165, 166, 167, 169, 170, 174, 178, 180, 188, 191, 197, 203, 206, 211, 215, 216, 219, 220, 221, 224, 226, 228, 229, 230, 231, 232, 233, 235, 239, 241, 242, 246, 251, 252, 253, 254, 256, 259, 261, 262, 264, 265, 267, 271, 273, 274, 275, 276, 277, 279, 280, 281, 283, 284, 285, 287, 288, 289, 295, 298, 299, 300, 302, 304, 307, 310, 311, 312, 314, 315, 316, 317, 318, 321, 327, 328, 332, 333, 334, 336, 338, 340, 344, 346, 347, 353, 354, 356, 358, 362, 364, 365, 369, 372, 373, 381, 383, 384, 386, 389, 391, 393, 394, 396, 399, 401, 402, 404, 406, 407, 408, 410, 411, 413, 416, 419, 421, 422, 423, 424, 425, 427, 428, 432, 433, 437, 438, 442, 444, 449, 452, 454, 455, 457, 458, 460, 462, 463, 465, 468, 469, 472, 474, 476, 479, 483, 486, 488, 490, 491, 492, 495, 500, 503, 504, 509, 515, 516, 518, 520, 521, 522, 523, 525, 528, 530, 531, 540, 541, 543, 546, 548, 553, 555, 556, 557, 559, 562, 563, 564, 565, 567, 568, 569, 572, 574, 575, 577, 578, 580, 582, 583, 584, 585, 588, 589, 590, 594, 596, 598, 600, 601, 602, 603, 605, 607, 608, 610, 611, 613, 614, 618, 619, 620, 621, 626, 630, 633, 634, 640, 646, 650, 651, 653, 654, 656, 658, 662, 663, 665, 666, 667, 670, 673, 676, 680, 685, 686, 687, 689, 691, 697, 699, 701, 702, 703, 704, 706, 709, 711, 714, 715, 716, 717, 720, 721, 722, 724, 725, 729, 731, 735, 736, 737, 740, 742, 754, 758, 760, 761, 764, 765, 767, 773, 775, 782, 783, 787, 790, 793, 795, 796, 797, 800, 801, 803, 806, 808, 809, 810, 811, 813, 818, 819, 822, 824, 825, 834, 835, 836, 838, 842, 843, 847, 849, 850, 851, 854, 855, 856, 858, 860, 863, 871, 874, 879, 882, 883, 885, 886, 888, 889, 894, 898, 901, 902, 904, 905, 906, 912, 917, 918, 920, 923, 924, 926, 927, 929, 934, 938, 939, 940, 946, 947, 951, 952, 954, 955, 958, 959, 964, 965, 966, 975, 985, 986, 988, 989, 991, 992, 995, 1001, 1005, 1011, 1012, 1014, 1016, 1019, 1020, 1023, 1031, 1033, 1034, 1037, 1038, 1039, 1040, 1042, 1043, 1046, 1047, 1050, 1052, 1054, 1062, 1063, 1065, 1071, 1073, 1075, 1076, 1078, 1079, 1080, 1081, 1082, 1087, 1089, 1090, 1095, 1099, 1100, 1106, 1107, 1108, 1110, 1111, 1112, 1115, 1116, 1118, 1119, 1124, 1126, 1127, 1128, 1133, 1136, 1140, 1142, 1143, 1147, 1148, 1153, 1157, 1158, 1159, 1160, 1162, 1165, 1169, 1171, 1188, 1190, 1192, 1194, 1197, 1198, 1200, 1205, 1206, 1207, 1208, 1210, 1211, 1213, 1216, 1217, 1220, 1221, 1227, 1228, 1231, 1236, 1237, 1248, 1250, 1252, 1253, 1254, 1255, 1261, 1262, 1268, 1271, 1272, 1275, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1297, 1299, 1300, 1301, 1311, 1313, 1315, 1316, 1318, 1324, 1325, 1326, 1327, 1328, 1335, 1336, 1339, 1340, 1342, 1343, 1352, 1355, 1357, 1360, 1361, 1362, 1363, 1365, 1372, 1374, 1375, 1377, 1379, 1385, 1386, 1398, 1399, 1400, 1406, 1418, 1419, 1421, 1422, 1423, 1424, 1425, 1426, 1428, 1429, 1431, 1433, 1434, 1442, 1452, 1453, 1454, 1455, 1456, 1460, 1461, 1462, 1463, 1470, 1473, 1474, 1479, 1480, 1493, 1494, 1495, 1497, 1500, 1501, 1502, 1503, 1509, 1510, 1512, 1515, 1517, 1524, 1526, 1535, 1536, 1540, 1542, 1543, 1544, 1548, 1549, 1551, 1558, 1559, 1560, 1565, 1566, 1568, 1571, 1572, 1573, 1575, 1580, 1583, 1587, 1590, 1594, 1595, 1597, 1605, 1606, 1609, 1610, 1618, 1619, 1620, 1621, 1626, 1631, 1635, 1636, 1639, 1640, 1650, 1651, 1652, 1653, 1660, 1673, 1674, 1676, 1677, 1680, 1681, 1682, 1683, 1690, 1691, 1694, 1695, 1707, 1708, 1709, 1710, 1711, 1713, 1714, 1717, 1718, 1720, 1721, 1723, 1726, 1727, 1729, 1731, 1739, 1741, 1743, 1754, 1755, 1761, 1762, 1764, 1769, 1771, 1775, 1778, 1780, 1782, 1785, 1786, 1787, 1797, 1803, 1807, 1808, 1811, 1813, 1817, 1818, 1821, 1824, 1826, 1827, 1833, 1835, 1838, 1839, 1840, 1845, 1846, 1848, 1851, 1852, 1856, 1857, 1864, 1865, 1866, 1867, 1872, 1873, 1874, 1880, 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/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.iloc[non_matched_indexes]['file_path']
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
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Drive/AllPapers/7_AAAJ/652_AAAJ.pdf Name: file_path, dtype: object
time: 239 ms
dt3c.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15219 entries, 0 to 15218 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15219 non-null object 1 Author 7808 non-null object 2 date 15006 non-null object 3 title 13571 non-null object 4 content_3 15219 non-null object 5 content_5 15219 non-null object 6 content_10 15219 non-null object 7 content_3_lang 15219 non-null object 8 content_5_lang 15219 non-null object 9 content_10_lang 15219 non-null object dtypes: object(10) memory usage: 1.2+ MB time: 117 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c = dt3c.drop(non_matched_indexes)
time: 19.9 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 11014 entries, 0 to 15218 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 11014 non-null object 1 Author 5799 non-null object 2 date 11002 non-null object 3 title 10051 non-null object 4 content_3 11014 non-null object 5 content_5 11014 non-null object 6 content_10 11014 non-null object 7 content_3_lang 11014 non-null object 8 content_5_lang 11014 non-null object 9 content_10_lang 11014 non-null object dtypes: object(10) memory usage: 946.5+ KB time: 17.2 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.reset_index(drop=True, inplace=True)
dt3c.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 11014 entries, 0 to 11013 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 11014 non-null object 1 Author 5799 non-null object 2 date 11002 non-null object 3 title 10051 non-null object 4 content_3 11014 non-null object 5 content_5 11014 non-null object 6 content_10 11014 non-null object 7 content_3_lang 11014 non-null object 8 content_5_lang 11014 non-null object 9 content_10_lang 11014 non-null object dtypes: object(10) memory usage: 860.6+ KB time: 16.2 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.to_pickle('en_cleaned_stop_words_lemmatized_3_5_10.pkl')
from wordcloud import WordCloud
wordcloud = WordCloud(
background_color='white',
stopwords=stop_list,
max_words=500,
max_font_size=40,
random_state=42
).generate(str(dt3c['content_10']))
print(wordcloud)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
<wordcloud.wordcloud.WordCloud object at 0x21d69c610> time: 3min 24s
import matplotlib.pyplot as plt
fig = plt.figure(1)
plt.imshow(wordcloud)
plt.axis('off')
plt.show();
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 280 ms
NUM_OF_TOPICS = 70
NUM_OF_WORDS_IN_EACH_TOPIC = 20
time: 620 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation
time: 633 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
from plotly.offline import plot
import plotly.graph_objects as go
import plotly.express as px
def get_top_n_words(corpus, n=None):
vec = CountVectorizer(stop_words=stop_list).fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
return words_freq[:n]
common_words = get_top_n_words(dt3c['content_10'], NUM_OF_WORDS_IN_EACH_TOPIC)
df2 = pd.DataFrame(common_words, columns = ['unigram' , 'count'])
fig = go.Figure([go.Bar(x=df2['unigram'], y=df2['count'])])
fig.update_layout(title=go.layout.Title(text="Top {} unigrams in the paper after removing stop words".format(NUM_OF_WORDS_IN_EACH_TOPIC)))
fig.show()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:382: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['002', 'ain', 'aren', 'bosnia', 'couldn', 'dist', 'female', 'genealogy', 'gov', 'hadn', 'hasn', 'herzegovina', 'http', 'isn', 'list_of_most_common_surnames', 'll', 'male', 'names', 'org', 'rep', 'shouldn', 've', 'wasn', 'weren', 'wiki', 'wikipedia', 'wouldn', 'www', 'yug'] not in stop_words.
time: 52.6 s
def get_top_n_bigram(corpus, n=None):
vec = CountVectorizer(ngram_range=(2, 2), stop_words=stop_list).fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
return words_freq[:n]
common_words = get_top_n_bigram(dt3c['content_10'], NUM_OF_WORDS_IN_EACH_TOPIC)
df3 = pd.DataFrame(common_words, columns = ['bigram' , 'count'])
fig = go.Figure([go.Bar(x=df3['bigram'], y=df3['count'])])
fig.update_layout(title=go.layout.Title(text="Top {} bigrams in the paper after removing stop words".format(NUM_OF_WORDS_IN_EACH_TOPIC)))
fig.show()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 2min 41s
"""
dt3c = pd.DataFrame(dt, columns=['file_path', 'Author', 'date', 'title'])
dt3c['content_3'] = dt['en_clean_content_3_stop_words_lemma']
dt3c.info()
unknown_lang_indexes = dt.index[dt['en_clean_content_3_lang'].str.match('UNKNOWN')]
dt3c = dt3c.drop(unknown_lang_indexes)
dt3c.info()
dt3c.reset_index(drop=True, inplace=True)
dt3c.info()
"""
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
"\ndt3c = pd.DataFrame(dt, columns=['file_path', 'Author', 'date', 'title'])\ndt3c['content_3'] = dt['en_clean_content_3_stop_words_lemma']\n\ndt3c.info()\n\nunknown_lang_indexes = dt.index[dt['en_clean_content_3_lang'].str.match('UNKNOWN')]\ndt3c = dt3c.drop(unknown_lang_indexes)\n\ndt3c.info()\ndt3c.reset_index(drop=True, inplace=True)\ndt3c.info()\n"
time: 3.89 ms
dt3c.info()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 Author 7794 non-null object 2 date 14976 non-null object 3 title 13543 non-null object 4 content_3 15189 non-null object 5 content_5 15189 non-null object 6 content_10 15189 non-null object 7 content_3_lang 15189 non-null object 8 content_5_lang 15189 non-null object 9 content_10_lang 15189 non-null object dtypes: object(10) memory usage: 1.2+ MB time: 3.7 s
import pyLDAvis
import pyLDAvis.sklearn
count_vectorizer = CountVectorizer(analyzer='word',
min_df=3,
stop_words=stop_list,
lowercase=True,
token_pattern='[a-zA-Z0-9]{3,}',
max_features=5000,
)
data_vectorized = count_vectorizer.fit_transform(dt3c['content_10'])
lda_model = LatentDirichletAllocation(n_components=NUM_OF_TOPICS, # Number of topics
learning_method='online',
random_state=0,
n_jobs = -1 # Use all available CPUs
)
lda_output = lda_model.fit_transform(data_vectorized)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:382: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['002', 'ain', 'aren', 'bosnia', 'common', 'couldn', 'dist', 'female', 'genealogy', 'gov', 'hadn', 'hasn', 'herzegovina', 'http', 'isn', 'list', 'male', 'names', 'ncipe', 'nomm', 'org', 'rdoba', 'rep', 'shouldn', 'taient', 'tat', 'tions', 'tre', 'wasn', 'weren', 'wiki', 'wikipedia', 'wouldn', 'www', 'yug'] not in stop_words.
time: 7min 18s
By default the topics are projected to the 2D plane using PCoA on a distance matrix created using the Jensen-Shannon divergence on the topic-term distributions. You can pass in a different multidimensional scaling function via the mds parameter. In addition to pcoa, other provided options are tsne and mmds which operate on the same JS-divergence distance matrix. Both tsne and mmds require that you have sklearn installed.
Dimension reduction via Jensen-Shannon Divergence &
We used tsne below. Check for others: https://nbviewer.jupyter.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb Also check to learn how LDAVis works: https://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf And check the LDAVis from the original paper: https://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf Why LDAVis presentation: https://speakerdeck.com/bmabey/visualizing-topic-models
pyLDAvis.enable_notebook()
pyLDAvis.sklearn.prepare(lda_model, data_vectorized, count_vectorizer, mds='tsne')
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 29.8 s
print(count_vectorizer)
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=5000, min_df=3,
ngram_range=(1, 1), preprocessor=None,
stop_words=frozenset({'(former', '.002%.', '1990', '>', 'a',
"a's", 'aaron', 'abbey', 'abbie',
'abbott', 'abby', 'abdul', 'abdullah',
'abe', 'abel', 'abell', 'abercrombie',
'abernathy', 'abeyta', 'abigail', 'able',
'ables', 'abner', 'abney', 'about',
'above', 'abraham', 'abram', 'abrams',
'abramson', ...}),
strip_accents=None, token_pattern='[a-zA-Z0-9]{3,}',
tokenizer=None, vocabulary=None)
time: 62.3 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
print(data_vectorized.toarray())
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
[[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 1 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]] time: 396 ms
topic_names = []
for i in range(NUM_OF_TOPICS):
topic_names.append("Topic {}".format(i+1))
time: 568 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def show_topics(vectorizer, lda_model, n_words):
keywords = np.array(vectorizer.get_feature_names())
topic_keywords = []
for topic_weights in lda_model.components_:
top_keyword_locs = (-topic_weights).argsort()[:n_words]
topic_keywords.append(keywords.take(top_keyword_locs))
return topic_keywords
topic_keywords = show_topics(vectorizer=count_vectorizer, lda_model=lda_model, n_words=NUM_OF_WORDS_IN_EACH_TOPIC)
df_topic_keywords = pd.DataFrame(topic_keywords)
df_topic_keywords.columns = ['Word '+str(i) for i in range(df_topic_keywords.shape[1])]
df_topic_keywords.index = ['Topic '+str(i) for i in range(df_topic_keywords.shape[0])]
df_topic_keywords['topic_theme'] = topic_names
df_topic_keywords.set_index('topic_theme', inplace=True)
df_topic_keywords.T
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
| topic_theme | Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | Topic 7 | Topic 8 | Topic 9 | Topic 10 | Topic 11 | Topic 12 | Topic 13 | Topic 14 | Topic 15 | Topic 16 | Topic 17 | Topic 18 | Topic 19 | Topic 20 | Topic 21 | Topic 22 | Topic 23 | Topic 24 | Topic 25 | Topic 26 | Topic 27 | Topic 28 | Topic 29 | Topic 30 | Topic 31 | Topic 32 | Topic 33 | Topic 34 | Topic 35 | Topic 36 | Topic 37 | Topic 38 | Topic 39 | Topic 40 | Topic 41 | Topic 42 | Topic 43 | Topic 44 | Topic 45 | Topic 46 | Topic 47 | Topic 48 | Topic 49 | Topic 50 | Topic 51 | Topic 52 | Topic 53 | Topic 54 | Topic 55 | Topic 56 | Topic 57 | Topic 58 | Topic 59 | Topic 60 | Topic 61 | Topic 62 | Topic 63 | Topic 64 | Topic 65 | Topic 66 | Topic 67 | Topic 68 | Topic 69 | Topic 70 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Word 0 | participant | market | datum | loss | asset | shareholder | tax | fraud | earning | analyst | management | organisation | corporate | option | gap | author | wage | woman | ownership | audit | target | accounting | news | auditor | performance | tax | error | regulation | individual | project | government | professional | study | risk | earning | student | manager | equity | quality | investment | debt | transfer | flow | standard | internal | company | disclosure | revenue | restatement | industry | model | research | variable | environmental | financial | return | customer | percent | social | firm | decision | director | communication | rating | public | profit | employee | cost | financial | research |
| Word 1 | judgment | share | number | asset | liability | vote | avoidance | fraudulent | accrual | forecast | process | organisational | governance | volatility | tournament | text | spanish | gender | governance | auditor | acquisition | account | conservatism | audit | manager | income | adjustment | adoption | personal | product | sector | accountant | survey | factor | valuation | education | interview | issue | inspection | capital | private | trust | uncertainty | accounting | control | opinion | information | hospital | filing | specialist | information | paper | model | sustainability | bank | investor | production | item | practice | industry | task | committee | language | credit | accountability | ratio | budget | benefit | report | accounting |
| Word 2 | condition | company | table | intangible | balance | election | taxpayer | detection | management | earning | organization | behaviour | ethical | exercise | reconciliation | style | transition | individual | control | auditing | merger | policy | insider | client | incentive | rate | misstatement | foreign | belief | technology | public | profession | group | business | future | university | study | debt | report | return | contract | subsidiary | future | financial | external | big | disclose | care | period | peer | assume | review | measure | report | security | market | service | report | form | market | effect | member | narrative | pension | political | growth | job | activity | information | theory |
| Word 3 | assessment | cent | study | gain | financial | voting | position | detect | firm | information | performance | recognise | business | executive | nancial | entry | vat | age | corporate | procedure | transaction | economic | bad | partner | compensation | dividend | materiality | list | attitude | competition | local | knowledge | company | assessment | gaap | school | process | security | improve | asset | covenant | joint | repurchase | set | management | concern | voluntary | health | file | level | agent | journal | test | social | crisis | announcement | supplier | segment | society | increase | information | executive | medium | rate | issue | profitability | performance | resource | reporting | study |
| Word 4 | investor | australian | index | income | sheet | share | aggressive | penalty | discretionary | recommendation | strategy | labour | responsibility | employee | crash | format | treaty | household | shareholder | audits | acquire | financial | event | big | measure | taxis | material | regulatory | generation | stage | reform | work | response | risky | component | academic | senior | issuer | pcaob | growth | information | transaction | reserve | base | weakness | report | company | service | financial | specific | function | article | estimate | stakeholder | banking | trading | product | firm | critical | effect | individual | financial | text | plan | policy | inventory | work | decision | statement | approach |
| Word 5 | manipulation | premium | analysis | impairment | net | sme | planning | enforcement | manager | issue | business | programme | company | underwriter | judgement | horizon | agreement | female | agency | work | acquirer | public | conservative | service | contract | effect | statement | market | people | development | political | member | respondent | exposure | model | research | role | issuance | improvement | rate | lender | division | estimate | principle | material | financial | csr | program | report | office | equilibrium | publish | result | emission | derivative | information | supply | total | accounting | result | study | independent | translation | benefit | report | margin | subordinate | allocation | user | management |
| Word 6 | experiment | insurance | score | goodwill | income | proposal | compliance | action | report | management | strategic | emphasise | ethic | offer | conformity | include | reduction | work | institutional | perform | purchase | include | negative | independence | pay | taxation | threshold | effect | trait | team | economy | skill | factor | model | current | faculty | management | offer | measure | invest | borrower | company | operating | issue | report | variable | investor | organization | restate | expertise | set | author | regression | company | market | trade | cost | increase | perspective | size | research | company | message | default | legal | total | level | capacity | company | researcher |
| Word 7 | effect | exchange | base | write | expense | approval | evasion | crime | manage | firm | control | organise | corporation | share | win | table | belgian | labor | family | practice | bid | practice | manager | increase | effort | pay | financial | adopt | personality | innovation | policy | experience | research | premium | return | teach | interviewee | share | review | term | public | venture | operate | framework | section | reputation | report | expenditure | increase | specialization | expect | issue | coefficient | carbon | loss | portfolio | demand | expense | work | study | result | expert | content | agency | government | analysis | worker | period | provide | method |
| Word 8 | perceive | period | category | accounting | statement | investor | authority | commit | abnormal | accuracy | organizational | analyse | independence | maj | finnish | electronic | downward | worker | firm | standard | bidder | management | conditional | study | base | foreign | difference | financial | psychological | company | service | cpa | question | level | persistence | institution | work | class | number | investor | financing | affiliate | component | statement | financial | bankruptcy | provide | patient | accelerate | market | optimal | academic | level | reporting | credit | period | chain | observation | actor | total | participant | expertise | tone | yield | account | average | organizational | base | investor | problem |
| Word 9 | result | finance | measure | sfa | account | proxy | aggressiveness | criminal | income | investor | information | characterise | code | seo | promotion | figure | upward | social | structure | review | method | change | timeliness | result | bonus | increase | correct | mandatory | behavioral | competitive | private | body | result | beta | period | business | meeting | common | public | market | leverage | party | current | process | effective | issue | study | increase | filer | measure | choose | publication | effect | issue | instrument | abnormal | relationship | panel | account | choice | hypothesis | officer | report | discount | action | operate | evaluation | rate | datum | practice |
| Word 10 | management | trade | total | recognition | accounting | pay | return | whistle | increase | revision | change | centre | moral | compensation | opacity | article | directive | culture | manager | process | auction | research | litigation | research | executive | taxable | correction | enforcement | optimism | market | economic | identity | cent | increase | income | learn | consultant | offering | identify | expect | term | entity | accrual | reporting | effectiveness | study | manager | nonprofit | turnover | firm | period | researcher | size | corporate | rate | result | output | period | process | control | evidence | governance | impression | retirement | act | result | budgeting | direct | decision | issue |
| Word 11 | question | list | rank | measurement | lease | advisor | strategy | brainstorm | period | guidance | activity | indigenous | issue | imply | level | reference | minimum | level | managerial | function | business | information | timely | evidence | increase | corporate | account | increase | confidence | cycle | network | service | stakeholder | specific | base | accounting | team | financing | inspect | finance | creditor | investment | firm | require | problem | receive | corporate | spend | include | concentration | probability | editor | table | impact | regulatory | volume | efficiency | result | paper | public | experience | meeting | communicate | contribution | claim | profitable | commitment | unit | internet | account |
| Word 12 | assess | offer | period | net | depreciation | influence | taxis | violation | smooth | research | structure | change | practice | ipo | group | double | earnout | family | monitor | assurance | combination | issue | market | tenure | ceo | gain | percent | institutional | optimistic | business | central | accounting | table | systematic | market | graduate | partner | raise | poor | project | firm | mutual | operation | entity | result | type | level | expense | section | digit | condition | conference | datum | sustainable | provision | event | consumer | change | human | hypothesis | experiment | management | document | define | private | return | satisfaction | increase | business | question |
| Word 13 | perception | takeover | author | report | method | pill | shelter | financial | incentive | error | model | summarise | role | base | result | copyright | tick | group | study | evidence | buyer | business | positive | engagement | managerial | policy | estimate | study | overconfidence | design | national | practice | perception | assess | measure | educational | conduct | placement | process | opportunity | credit | control | ability | setter | auditor | firm | increase | donor | letter | leader | result | include | correlation | climate | risk | reaction | contract | income | economic | number | outcome | independence | audience | expect | establish | table | relationship | production | web | work |
| Word 14 | experimental | bid | result | future | current | voter | difference | opportunity | evidence | announcement | level | realise | relationship | expect | prize | sex | study | investor | internal | seller | finance | press | report | agent | marginal | detect | standard | behavior | software | level | public | report | portfolio | expect | program | staff | convertible | deficiency | equity | cost | group | predict | concept | reporting | period | performance | medical | fiscal | sic | increase | editorial | base | change | increase | average | unit | compustat | institutional | list | subject | number | statement | funding | party | current | organization | charge | include | paper | |
| Word 15 | provide | author | list | charge | property | poison | benefit | identify | total | coverage | resource | behavioural | financial | underprice | difference | submit | offer | male | monitoring | require | payment | accountant | information | provide | principal | payment | result | regime | preference | industry | enterprise | training | perceive | pricing | investor | degree | datum | finance | inspector | increase | financial | consolidation | provide | economic | function | total | mandatory | spending | control | base | utility | literature | include | water | asset | firm | input | table | action | variable | affect | appointment | credibility | assumption | form | turnover | study | level | require | perspective |
| Word 16 | suggest | study | publishing | recognize | operate | management | research | forensic | study | surprise | study | utilise | act | model | synchronicity | number | funder | difference | owner | provide | evidence | review | increase | rotation | period | shift | adjust | investor | photograph | life | region | role | influence | inherent | dividend | teaching | formal | secondary | finding | decision | contracting | profit | net | approach | level | failure | content | donation | reporting | share | assumption | work | statistic | oil | economic | trader | labor | asset | change | affect | process | monitor | readability | term | support | datum | measure | incur | section | point |
| Word 17 | manipulate | open | classification | estimate | defer | company | activity | behavior | result | expectation | focus | colonial | stakeholder | reprice | eect | effort | employment | problem | include | announcement | section | release | issue | effect | reduce | test | difference | film | manufacturing | corruption | accountancy | item | base | firm | college | practice | structure | result | flow | default | host | uncertain | gaap | deficiency | size | release | total | end | effect | signal | area | study | gas | institution | liquidity | buyer | number | role | examine | behavior | study | sentence | grade | regulation | net | budgetary | total | content | analysis | |
| Word 18 | response | result | paper | financial | recognize | activist | expense | scheme | manipulation | provide | implementation | islamic | management | expense | rank | write | beneficiary | population | level | statement | exit | receive | return | litigation | ceos | incentive | require | legal | motive | competitor | fiscal | charter | significant | research | pro | class | involve | outstanding | deficiencie | level | asymmetry | alliance | require | conceptual | require | model | management | provide | match | knowledge | type | accounting | observation | performance | capital | spread | store | include | theory | asset | experimental | experience | write | actuarial | provide | difference | employer | estimate | research | literature |
| Word 19 | panel | insurer | data | comprehensive | payment | institutional | measure | probability | current | manager | base | favour | service | eso | increase | copy | unlisted | home | mechanism | conduct | gain | role | measure | level | level | base | type | domestic | emotional | marketing | resource | practitioner | pressure | approach | information | professor | face | proceed | firm | expenditure | increase | method | classification | apply | compliance | distress | measure | public | event | study | private | citation | hypothesis | biodiversity | mortgage | size | base | fiscal | individual | specific | theory | role | juror | increase | require | trend | superior | reduce | present | view |
time: 205 ms
# Create Document - Topic Matrix
lda_output2 = lda_model.transform(data_vectorized)
# column names
topicnames = df_topic_keywords.T.columns
# index names
docnames = ["Doc" + str(i) for i in range(len(dt3c))]
# Make the pandas dataframe
df_document_topic = pd.DataFrame(np.round(lda_output2, 2), columns=topicnames, index=docnames)
# Get dominant topic for each document
dominant_topic = np.argmax(df_document_topic.values, axis=1)
df_document_topic['dominant_topic'] = dominant_topic
df_document_topic.reset_index(inplace=True)
df_sent_topic= pd.merge(dt3c, df_document_topic, left_index=True, right_index=True)
df_sent_topic.drop('index', axis=1, inplace=True)
df_topic_theme = df_sent_topic[['file_path', 'dominant_topic']]
def label_theme(row):
return "Topic " + str(row)
df_topic_theme['dominant_topic_theme'] = df_topic_theme['dominant_topic'].apply(label_theme)
df_topic_theme.head(15)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. <ipython-input-216-9689e41583c7>:26: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
| file_path | dominant_topic | dominant_topic_theme | |
|---|---|---|---|
| 0 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/711_AH.pdf | 10 | Topic 10 |
| 1 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/140_AH.pdf | 69 | Topic 69 |
| 2 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/611_AH.pdf | 13 | Topic 13 |
| 3 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/294_AH.pdf | 43 | Topic 43 |
| 4 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/123_AH.pdf | 18 | Topic 18 |
| 5 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/672_AH.pdf | 9 | Topic 9 |
| 6 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/394_AH.pdf | 23 | Topic 23 |
| 7 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/398_AH.pdf | 22 | Topic 22 |
| 8 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/298_AH.pdf | 19 | Topic 19 |
| 9 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/603_AH.pdf | 45 | Topic 45 |
| 10 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/152_AH.pdf | 27 | Topic 27 |
| 11 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/703_AH.pdf | 56 | Topic 56 |
| 12 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/386_AH.pdf | 49 | Topic 49 |
| 13 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/660_AH.pdf | 15 | Topic 15 |
| 14 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/131_AH.pdf | 44 | Topic 44 |
time: 23 s
print(lda_output.shape)
topic_sum = pd.DataFrame(np.sum(lda_output, axis = 1))
print(topic_sum.shape)
(15189, 70) (15189, 1) time: 3.67 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
column_names = ['file_path', 'content_10'] + topic_names + ['sum']
# Turn our docs_nmf array into a data frame
doc_topic_df = pd.DataFrame(data = lda_output)
# Merge all of our article metadata and name columns
doc_topic_df = pd.concat([dt3c[['file_path', 'content_10']],
doc_topic_df, topic_sum], axis = 1)
time: 66.3 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
doc_topic_df.columns = column_names
time: 695 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
doc_topic_df.head(1)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
| file_path | content_10 | Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | Topic 7 | Topic 8 | Topic 9 | Topic 10 | Topic 11 | Topic 12 | Topic 13 | Topic 14 | Topic 15 | Topic 16 | Topic 17 | Topic 18 | Topic 19 | Topic 20 | Topic 21 | Topic 22 | Topic 23 | Topic 24 | Topic 25 | Topic 26 | Topic 27 | Topic 28 | Topic 29 | Topic 30 | Topic 31 | Topic 32 | Topic 33 | Topic 34 | Topic 35 | Topic 36 | Topic 37 | Topic 38 | Topic 39 | Topic 40 | Topic 41 | Topic 42 | Topic 43 | Topic 44 | Topic 45 | Topic 46 | Topic 47 | Topic 48 | Topic 49 | Topic 50 | Topic 51 | Topic 52 | Topic 53 | Topic 54 | Topic 55 | Topic 56 | Topic 57 | Topic 58 | Topic 59 | Topic 60 | Topic 61 | Topic 62 | Topic 63 | Topic 64 | Topic 65 | Topic 66 | Topic 67 | Topic 68 | Topic 69 | Topic 70 | sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/711_AH.pdf | adoption experience choice synopsis exist study linkage institutional factor adoption literature overlook important question decision implement influence institutional factor paper author argue decision adopt system influence equally institutional factor examine characteristic moderate effect institutional factor adoption decision specifically decision adopt enterprise resource plan ne result study reveal institutional factor mimicry peer compliance industry norm coercion powerful entity adoption decision result effect institutional factor adoption decision amplify participant adopt benefit difficult quantify enhance organizational interaction supply chain paper author contribute institutional theory literature explore dition enhance salience institutional factor contribute account information system literature investigate boundary influence insti tutional factor adoption institutional theory technology acceptance contact author copy datum introduction ensible technology organization competitive advantage enterprise resource plan system capable transforming organizational process system provide firm positive return lway system costly result inconsistent economic return constructive guidance proposal present information system section midyear meeting research symposium pro iding direction finally reviewer account horizon invaluable assistance submit accept publish online author poor mplementation poor articular firm adopt motivated econd reason couple frequent failure system author explore doption decision process account research primarily explore outcome ffect study nicolaou summary ome recent empirical research nicolaou conversely author teps primary foci examine firm adopt adoption information system investigate decade exist model technology adoption diffusion link complex etwork organizational individual social factor adoption decision ord recent research show information system institutional factor complement ystematic cost benefit analysis teo understanding effect institutional actor important adoption decision primarily base institutional factor eemingly result limited understanding contribute specific organization systematic analysis implementation firm estructure process implementation economical exist pro esse worrisome firm adopt system involve sizable nvestment system connection institutional factor financial study investigate impact institutional factor adoption examine condition institutional factor effectual paper suggest implementation implemen ation degree uncertainty identify quantify parameter benefit solution modern institutional theory suggest firm uncertainty traditional cost benefit analyse institutional factor legitimize decision study examine role institutional factor raditional decision variable expect benefit experience manager decision legitimize action include conformity stablishe norm surrender coercion powerful entity researcher include organizational behavior accounting examine issue institutional perspective lens tudy concern system doption literature recently incorporate institutional call attention institutional perspective offer researcher vantage point eptualize digital economy emergent evolve embed fragmented provi ional social production shape cultural structural force economic teo provide evidence institutional factor affect information ystem adoption decision question remain effect institutional factor doption decision institutional literature information system largely focus ffect institutional factor system adoption decision decision maker jeyariaj leave unanswered uestion firm influence institutional factor system ccounte horizon choice institutional factor influence adoption decision type system equally hat unique characteristics decision adopt highly usceptible influence institutional factor examine hese question measure relationship perception institutionalization experience stage firm reach adoption decision result mimetic normative factor influence decision adopt mimetic factor pronounce perceive benefit adopt ystem difficult quantify adoption decision maker work firm adopt normative factor pronounce perceive integrate firm supply chain paper contribute system ure broadening understand institutional factor influence institutional theory literature testing boundary institutional theory exam ning variable situation decision adopt system susceptible nstitutional influence background nstitutional theory industry organizational structure evolve autonomy social interconnection bud characterize arket innovation firm shape market create niche creativity differentiation market mature organization rise social political echanism develop mature market characterize social environment nnovation supplant legitimizing action organization attempt adopt mechanism successful organization promote social norm group gain advantage begin accumulate resource organizational stratifie institutional legitimacy rise behavior process procedure routine facilitate stability routine behavior decouple competitive measure ceremonial political influence force organizational create pressure adopt behavior similar successful entity company institutional factor rationalize process seek lleviate uncertainty examine economic institutional factor word orga ization decision base internal external variable economic factor nclude rational cost benefit analysis relative economic advantage implement tech ology industry mature rational economic base choice complement ationalize social base choice socially develop norm arise rant relied fact shape future organizational action institutional factor play organization construct process hat include perceive legitimate organization conformity establish norm ompliance coercive pressure rise organization increasingly omogeneous increase homogeneity isomorphism amplifie nstitutional pressure result great define institutionalization process innovation regular work behavior suggest e institutionalization adoption usage ccounte horizon occur organization model involve organization eliminate ncertainty imitating organization perceive legitimate auspicious ther word firm mimic successful firm mimicry help alleviate insecurity cause mbiguity confidence decision maker idea explain wide array business decision include business structure govern ental policy merging organization entrance economically advantageous conduct complete cost benefit nalysis lead successful firm save resource mimic rganization failure perform analysis mimicking firm aware situation strategy organization cyert additionally successful firm mimic level firm ndustry focus create type competitive advantage illard level organization mimic limit oppor unity technological innovation create competitive advantage conformity establish norm occur organization comply conform orm develop firm professional organization frequently interact ocial contagion literature suggest contact communication mechanism ndividual organization communication increase likelihood individual organization imitate teo suggest focal organization direct indirect tie organization adopt inno vation learn innovation benefit cost persuade behave similarly share norm relational channel member network facilitate consensus turn increase strength norm potential influence organizational behavior normative pressure manifest dyadic inter organizational channel firm supplier customer professional trade business organization membership involvement professional trade business organiza ion bring normative pressure teo entity employ ndividual belong specific group organization entity influence ormative factor popularize group illustrate professionalism member ancial accounting standard board emerge issue task task member hare similar body knowledge issue reach decision quickly ebate suggest normative factor compliance coercive factor occur organization adopt structure technology ecause dominant customer supplier entity government funding source firm compel adopt technology pressure perceive exert norm society organization firm depend dimag organizational literature show example coercive factor nfluencing firm structure fligstein equal opportunity employ ent insurance industry corporate social response green finally institutional researcher argue institutional factor affect late stage adopt strategy affect stage adopter stage adopter encounter nstitutional pressure pressure influence late stage adopter arly stage adopter decision rational analysis aim improve operational ccounte horizon choice rganizational effectiveness rumelt zucker exist tudie incorporate institutional theory adoption fail account stage firm adopt recently institutional theory apply accounting information accounting researcher examine wide variety issue institutional study publish mid examined accounting information support decision institutional examine impact institutional factor choice account policy recent esearch apply institutional theory firm choice performance change scapen increase homogeneity accounting research information system researcher apply institutional theory frequently teo advocate note dominant paradigm surround technology adoption esearch information system firm determine decision adopt tech ology base quality influence doption decision technological quality include social institutional factor impor ant linger question information system research force ion strong variable contagion effect mention institutional theory examine social institu ional influence technology adoption decision propose institutional factor adoption business application adoption large strategic partner organization industry conceptual paper literature extend imite evidence institutional factor system adoption rise research question institutional factor strategy implementation practice process scapen suggest significant influence factor future direction include development summary critical issue strategy mechanism implement strategy dechow mouritsen study typifie examine influence integration uggest integration develop difficult decouple technology strategy technological function imitation system influence echnology interesting aspect dechow study illustrate echnology strategy decouple suggest firm implement boundary object vercome base blind spot illustrate division strategy technology word system facilitate integration meet rganization mechanism implement hortfall nonetheless practice embed system tend spread organization hat adopt system response change environment institutional perspective ccounte horizon rganization embed complex change environment adopt structure contin ent current situation traditional institutional factor seemingly play role echnology advance couple organizational ecosystem development strategy management practice mechanism firm adopt strategy environment stage firm follow task fit complement institutional factor stage nstitutional information absence legitimate structure stage dopter innovator decide structure technology base aim gain legitimacy improvement operational organizational efficiency ucker rumelt researcher attempt illustrate stage adop ion fit adopt compare late stage adoption study civil service reform show intra organizational actor decrease role obtain imilar result study effect manager network adoption innovative anagement practice total quality management rganization adopt multidivisional structure teo point failure study conjecture organization perceive system widely adopt industry institutional factor strongly adoption decision relative hen organization widely adopt suggest firm perceive extent adoption industry moderate effect institutional factor firm rise perception extent system adopt decision maker industry moderate effect institutional factor decision maker characteristic ystem benefit difficult quantify adoption literature extensively examine perception ion user perception relative advantage compatibility omplexity observability ease image benbasat govern rate pattern technology adoption usage variable primarily view predictor adoption individually interact work nstitutional factor institutional literature clue factor enhance institutional organization succumb institutional pressure goal mbiguous end relationship uncertain word great complexity result uncertainty result great eliance factor make adoption decision include institutional factor onjecture information system complex benefit adoption ifficult quantify decision adopt susceptible institutional actor benefit system show difficult quantify adoption enterprise system literature note system complex notorious benefit hard quantify make adoption ecision set goal implementation project ambiguity nfluence adopter organization information ecide propose ambiguous goal uncer ccounte horizon choice end relationship lead mimicry reason rganization ambiguous goal dependent appearance legitimacy esort modeling organization perceive legitimate meet expectation rominent organizational partner conflict goal arise organization mimic arduous systematic analysis building logic onjecture benefit adopt difficult quantify implementation oal ambiguous leading uncertainty adoption implementation outcome enhance effect mimetic factor adoption decision plausible hat ambiguity benefit influence adopter norm easily oerce firm follow reluctant disaffiliate ndustry norm structure stipulate dominant entity discussion suggest effect institutional pressure adoption decision moderate level ambiguity surround cost benefit adopt deduction test motivate perception difficult quantify ability achieve organizational objective moderate effect institutional factor decision maker ystem integrate organization supply chain traditional model diffusion assume firm adopt technology independently interconnect network organization technology intertwine organizational routine integrate number individual rganization diffuse unit economic reason fichman technology increase interaction firm perceive potential cost saver supply chain interaction interconnectedness result hare resource ease information exchange operating cost perception hat cost saving result firm implement structure increase interconnection exist network adopter economic benefit adopt technology se exacerbate suggest technology eache tip point point technology widely adopt tandard failure follow standard negative economic consequence ikely adopt network interconnectedness secondary component influence firm institutional actorsinformation exchange organization institutionalize interaction mutual awareness increase organization explain social contagion literature examine interaction rganization inter organizational network suggest knowledge attitude behavior firm knowledge attitude behavior heir network contagion enhance interaction great additionally social contagion group facilitate single ndividual act agent increase interaction organization learn another intention problem olution facilitate imitation note dependence lead isomorphic change coercive pressure build exchange relationship imply hat frequent exchange relationship lead increase influence coercive factor nteraction employee customer supplier consultant agency rease interconnectedness mutual awareness organization pread institutionalize norm teo ccounte horizon suggest thisbut explicitly test itin system context state financial independently adopt financial edi success depend illingness adopting organization supplier customer accede electronic linkage common base adjacent discussion conjecture organizational system explore explore interaction mutual wareness organization conjecture institutional factor nfluential adopt system economic reason ion exchange institutional factor affect factor ance interaction supply chain expectation lead attempt separate effect information exchange firm nce great degree information effect assumption ntegration economically advantageous adoption result integration lead cost aving interconnect firm lead perception ability integrate firm supplier customer moderate effect institutional factor decision maker implement perception integrate firm supplier customer moderate effect institutional factor decision maker implement extent integration perceive economically advantageous experience final question explore paper participant experience nhance effect institutional factor suggest educate technology education interact knowledge individual ork firm adopt influence external factor adopt factor bound rationality articipant limited knowledge heuristic possibly peer nfluential party assist make judgment decision reasonable conjecture nstitutional factor great adoption decision experience general firm specifically lead final set research question experience influence effect nstitutional factor effect institutional factor decision maker moderate decision maker experience effect institutional factor decision maker amplified decision maker firm mente research model present figure institutional factor influence adoption ntention directly firm stage adoption ant experience moderate relationship ccounte horizon choice method research question test survey measure participant perceive evel institutional factor mimetic normative coercive perception ifficulty quantify benefit adopt difficulty perceive integrate organization supply chain integrativeness participant perceive ntegrativeness benefit adopt doption stage experience experience ddition participant firm employ adopt adopt teo measure institutional factor ere developed measure primary variable perceive ntegrativeness perceive difficulty quantify benefit scale elopment procedure outline single item measure benefit tage experience measure collect information participant number additional survey item capture general perceive benefit highly correlate intention construct deem inseparable perceive benefit research model institutional factor stage adoption symbolize text table interaction institutional factor difficulty quantify benefit bolized text table factor integrativeness symbolize text table factor perceive benefit integration symbolize text table interaction institutional factor experience symbolize text table interaction institutional factor participant current firm adopt symbolize text table ccounte horizon experience finally participant current firm dopte dependent variable measure participant adopt adoption intent base theory easone action fishbein theory plan behavior ajzen diffusion nnovation acceptance model unify theory acceptance technology venkatesh intention precede action sum ary variable measure present table table variable measure ariable measure measurement source variable mimetic extent adoption competitor teo adoption supplier teo extent adoption customer item cronbachs customer item cronbachs ependent variable adoption intent likelihood newly create item oderating variable difficulty quantify difficulty quantify system benefit achieve organization strategic goal operational goal newly create item cronbachs integrativeness extent lead increase integration information share coordination business operation coordination strategic planning activity company newly create item cronbachs benefit benefit integration newly create item stage stage system adopt general newly create item experience experience system year newly create item adopt respondent current company adopt newly create item dimension normative relevant drop participant belong relevant trade group dimension coercive factorconformity company drop participant manager work firm company work company ccounte horizon | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000802 | 0.000007 | 0.000007 | 0.000007 | 0.261818 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.019234 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.180589 | 0.000007 | 0.030038 | 0.000007 | 0.003475 | 0.131779 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.015545 | 0.000007 | 0.000007 | 0.009937 | 0.000007 | 0.000007 | 0.000007 | 0.023314 | 0.000007 | 0.11752 | 0.039827 | 0.155653 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.000007 | 0.01006 | 0.000007 | 0.000007 | 1.0 |
time: 57.4 ms
doc_topic_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 73 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 content_10 15189 non-null object 2 Topic 1 15189 non-null float64 3 Topic 2 15189 non-null float64 4 Topic 3 15189 non-null float64 5 Topic 4 15189 non-null float64 6 Topic 5 15189 non-null float64 7 Topic 6 15189 non-null float64 8 Topic 7 15189 non-null float64 9 Topic 8 15189 non-null float64 10 Topic 9 15189 non-null float64 11 Topic 10 15189 non-null float64 12 Topic 11 15189 non-null float64 13 Topic 12 15189 non-null float64 14 Topic 13 15189 non-null float64 15 Topic 14 15189 non-null float64 16 Topic 15 15189 non-null float64 17 Topic 16 15189 non-null float64 18 Topic 17 15189 non-null float64 19 Topic 18 15189 non-null float64 20 Topic 19 15189 non-null float64 21 Topic 20 15189 non-null float64 22 Topic 21 15189 non-null float64 23 Topic 22 15189 non-null float64 24 Topic 23 15189 non-null float64 25 Topic 24 15189 non-null float64 26 Topic 25 15189 non-null float64 27 Topic 26 15189 non-null float64 28 Topic 27 15189 non-null float64 29 Topic 28 15189 non-null float64 30 Topic 29 15189 non-null float64 31 Topic 30 15189 non-null float64 32 Topic 31 15189 non-null float64 33 Topic 32 15189 non-null float64 34 Topic 33 15189 non-null float64 35 Topic 34 15189 non-null float64 36 Topic 35 15189 non-null float64 37 Topic 36 15189 non-null float64 38 Topic 37 15189 non-null float64 39 Topic 38 15189 non-null float64 40 Topic 39 15189 non-null float64 41 Topic 40 15189 non-null float64 42 Topic 41 15189 non-null float64 43 Topic 42 15189 non-null float64 44 Topic 43 15189 non-null float64 45 Topic 44 15189 non-null float64 46 Topic 45 15189 non-null float64 47 Topic 46 15189 non-null float64 48 Topic 47 15189 non-null float64 49 Topic 48 15189 non-null float64 50 Topic 49 15189 non-null float64 51 Topic 50 15189 non-null float64 52 Topic 51 15189 non-null float64 53 Topic 52 15189 non-null float64 54 Topic 53 15189 non-null float64 55 Topic 54 15189 non-null float64 56 Topic 55 15189 non-null float64 57 Topic 56 15189 non-null float64 58 Topic 57 15189 non-null float64 59 Topic 58 15189 non-null float64 60 Topic 59 15189 non-null float64 61 Topic 60 15189 non-null float64 62 Topic 61 15189 non-null float64 63 Topic 62 15189 non-null float64 64 Topic 63 15189 non-null float64 65 Topic 64 15189 non-null float64 66 Topic 65 15189 non-null float64 67 Topic 66 15189 non-null float64 68 Topic 67 15189 non-null float64 69 Topic 68 15189 non-null float64 70 Topic 69 15189 non-null float64 71 Topic 70 15189 non-null float64 72 sum 15189 non-null float64 dtypes: float64(71), object(2) memory usage: 8.5+ MB time: 31.2 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
# Remove articles with topic sum = 0, then drop sum column
doc_topic_df = doc_topic_df[doc_topic_df['sum'] != 0]
doc_topic_df.drop(columns = 'sum', inplace = True)
# Reset index then save
doc_topic_df.reset_index(drop = True, inplace = True)
#doc_topic_df.to_pickle('counter_vectorizer_LDA_8topics_stemmed.pkl')
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 11.1 ms
doc_topic_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 72 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 content_10 15189 non-null object 2 Topic 1 15189 non-null float64 3 Topic 2 15189 non-null float64 4 Topic 3 15189 non-null float64 5 Topic 4 15189 non-null float64 6 Topic 5 15189 non-null float64 7 Topic 6 15189 non-null float64 8 Topic 7 15189 non-null float64 9 Topic 8 15189 non-null float64 10 Topic 9 15189 non-null float64 11 Topic 10 15189 non-null float64 12 Topic 11 15189 non-null float64 13 Topic 12 15189 non-null float64 14 Topic 13 15189 non-null float64 15 Topic 14 15189 non-null float64 16 Topic 15 15189 non-null float64 17 Topic 16 15189 non-null float64 18 Topic 17 15189 non-null float64 19 Topic 18 15189 non-null float64 20 Topic 19 15189 non-null float64 21 Topic 20 15189 non-null float64 22 Topic 21 15189 non-null float64 23 Topic 22 15189 non-null float64 24 Topic 23 15189 non-null float64 25 Topic 24 15189 non-null float64 26 Topic 25 15189 non-null float64 27 Topic 26 15189 non-null float64 28 Topic 27 15189 non-null float64 29 Topic 28 15189 non-null float64 30 Topic 29 15189 non-null float64 31 Topic 30 15189 non-null float64 32 Topic 31 15189 non-null float64 33 Topic 32 15189 non-null float64 34 Topic 33 15189 non-null float64 35 Topic 34 15189 non-null float64 36 Topic 35 15189 non-null float64 37 Topic 36 15189 non-null float64 38 Topic 37 15189 non-null float64 39 Topic 38 15189 non-null float64 40 Topic 39 15189 non-null float64 41 Topic 40 15189 non-null float64 42 Topic 41 15189 non-null float64 43 Topic 42 15189 non-null float64 44 Topic 43 15189 non-null float64 45 Topic 44 15189 non-null float64 46 Topic 45 15189 non-null float64 47 Topic 46 15189 non-null float64 48 Topic 47 15189 non-null float64 49 Topic 48 15189 non-null float64 50 Topic 49 15189 non-null float64 51 Topic 50 15189 non-null float64 52 Topic 51 15189 non-null float64 53 Topic 52 15189 non-null float64 54 Topic 53 15189 non-null float64 55 Topic 54 15189 non-null float64 56 Topic 55 15189 non-null float64 57 Topic 56 15189 non-null float64 58 Topic 57 15189 non-null float64 59 Topic 58 15189 non-null float64 60 Topic 59 15189 non-null float64 61 Topic 60 15189 non-null float64 62 Topic 61 15189 non-null float64 63 Topic 62 15189 non-null float64 64 Topic 63 15189 non-null float64 65 Topic 64 15189 non-null float64 66 Topic 65 15189 non-null float64 67 Topic 66 15189 non-null float64 68 Topic 67 15189 non-null float64 69 Topic 68 15189 non-null float64 70 Topic 69 15189 non-null float64 71 Topic 70 15189 non-null float64 dtypes: float64(70), object(2) memory usage: 8.3+ MB time: 17.1 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
doc_topic_df[['file_path']+topic_names].head(3).T
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
| 0 | 1 | 2 | |
|---|---|---|---|
| file_path | /Users/farukcankaya/Google Drive/AllPapers/8_AH/711_AH.pdf | /Users/farukcankaya/Google Drive/AllPapers/8_AH/140_AH.pdf | /Users/farukcankaya/Google Drive/AllPapers/8_AH/611_AH.pdf |
| Topic 1 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 2 | 7.31475e-06 | 7.61499e-06 | 0.0241767 |
| Topic 3 | 7.31475e-06 | 0.0028478 | 0.0108006 |
| Topic 4 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 5 | 7.31475e-06 | 7.61499e-06 | 0.0014224 |
| Topic 6 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 7 | 0.000801635 | 0.000698363 | 7.6476e-06 |
| Topic 8 | 7.31475e-06 | 7.61499e-06 | 0.000687872 |
| Topic 9 | 7.31475e-06 | 7.61499e-06 | 0.0114476 |
| Topic 10 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 11 | 0.261818 | 0.0928088 | 7.6476e-06 |
| Topic 12 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 13 | 7.31475e-06 | 0.0110603 | 7.6476e-06 |
| Topic 14 | 7.31475e-06 | 7.61499e-06 | 0.228456 |
| Topic 15 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 16 | 0.0192336 | 0.0198853 | 0.00194571 |
| Topic 17 | 7.31475e-06 | 7.61499e-06 | 0.00314341 |
| Topic 18 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 19 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 20 | 7.31475e-06 | 0.0283297 | 7.6476e-06 |
| Topic 21 | 7.31475e-06 | 7.61499e-06 | 0.00335467 |
| Topic 22 | 7.31475e-06 | 0.065795 | 0.0113 |
| Topic 23 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 24 | 7.31475e-06 | 7.61499e-06 | 0.222249 |
| Topic 25 | 7.31475e-06 | 0.0047113 | 7.6476e-06 |
| Topic 26 | 7.31475e-06 | 0.00679745 | 7.6476e-06 |
| Topic 27 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 28 | 0.180589 | 7.61499e-06 | 7.6476e-06 |
| Topic 29 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 30 | 0.0300377 | 0.01789 | 7.6476e-06 |
| Topic 31 | 7.31475e-06 | 0.0505968 | 7.6476e-06 |
| Topic 32 | 0.00347533 | 0.0262994 | 7.6476e-06 |
| Topic 33 | 0.131779 | 0.00287006 | 7.6476e-06 |
| Topic 34 | 7.31475e-06 | 0.00337469 | 7.6476e-06 |
| Topic 35 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 36 | 7.31475e-06 | 0.124504 | 0.00192392 |
| Topic 37 | 7.31475e-06 | 0.00693609 | 0.00727144 |
| Topic 38 | 7.31475e-06 | 7.61499e-06 | 0.0662418 |
| Topic 39 | 7.31475e-06 | 0.00274285 | 0.0257856 |
| Topic 40 | 7.31475e-06 | 0.002828 | 0.00371269 |
| Topic 41 | 7.31475e-06 | 0.00295837 | 7.6476e-06 |
| Topic 42 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 43 | 7.31475e-06 | 0.00219005 | 7.6476e-06 |
| Topic 44 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 45 | 7.31475e-06 | 0.00746239 | 7.6476e-06 |
| Topic 46 | 7.31475e-06 | 7.61499e-06 | 0.0858027 |
| Topic 47 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 48 | 7.31475e-06 | 0.00562111 | 7.6476e-06 |
| Topic 49 | 7.31475e-06 | 7.61499e-06 | 0.0177127 |
| Topic 50 | 0.0155451 | 0.00622411 | 7.6476e-06 |
| Topic 51 | 7.31475e-06 | 0.0390976 | 7.6476e-06 |
| Topic 52 | 7.31475e-06 | 0.0459626 | 7.6476e-06 |
| Topic 53 | 0.00993704 | 0.0235319 | 0.104069 |
| Topic 54 | 7.31475e-06 | 0.0205753 | 7.6476e-06 |
| Topic 55 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 56 | 7.31475e-06 | 7.61499e-06 | 0.0851191 |
| Topic 57 | 0.0233141 | 0.0208999 | 7.6476e-06 |
| Topic 58 | 7.31475e-06 | 7.61499e-06 | 0.0776207 |
| Topic 59 | 0.11752 | 0.026269 | 7.6476e-06 |
| Topic 60 | 0.0398271 | 7.61499e-06 | 7.6476e-06 |
| Topic 61 | 0.155653 | 0.00869833 | 7.6476e-06 |
| Topic 62 | 7.31475e-06 | 0.00683643 | 7.6476e-06 |
| Topic 63 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 64 | 7.31475e-06 | 7.61499e-06 | 7.6476e-06 |
| Topic 65 | 7.31475e-06 | 0.0572484 | 7.6476e-06 |
| Topic 66 | 7.31475e-06 | 0.00509927 | 7.6476e-06 |
| Topic 67 | 7.31475e-06 | 0.0190167 | 7.6476e-06 |
| Topic 68 | 0.0100595 | 0.0179925 | 7.6476e-06 |
| Topic 69 | 7.31475e-06 | 0.007655 | 0.00538801 |
| Topic 70 | 7.31475e-06 | 0.205433 | 7.6476e-06 |
time: 25.1 ms
topic_array = np.array(doc_topic_df[topic_names])
print(topic_array.shape)
print(topic_array[0])
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
(15189, 70) [7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 8.01634863e-04 7.31475386e-06 7.31475386e-06 7.31475386e-06 2.61818052e-01 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 1.92335509e-02 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 1.80589093e-01 7.31475386e-06 3.00377489e-02 7.31475386e-06 3.47533119e-03 1.31779386e-01 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 1.55450787e-02 7.31475386e-06 7.31475386e-06 9.93703927e-03 7.31475386e-06 7.31475386e-06 7.31475386e-06 2.33140919e-02 7.31475386e-06 1.17520011e-01 3.98270942e-02 1.55652729e-01 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 7.31475386e-06 1.00595322e-02 7.31475386e-06 7.31475386e-06] time: 14.2 ms
Save the model to use as pre-trained model on the https://simple-recommender.herokuapp.com/ website.
import pickle
topic_names = []
for i in range(NUM_OF_TOPICS):
topic_names.append("Topic {}".format(i+1))
topic_array = np.array(doc_topic_df[topic_names])
norms = np.linalg.norm(topic_array, axis = 1)
pickle.dump( norms, open( "model__norms.pkl", "wb" ) )
pickle.dump( topic_array, open( "model__topic_array.pkl", "wb" ) )
df_topic_keywords.to_pickle('model__df_topic_keywords.pkl')
pickle.dump( doc_topic_df, open( "model__doc_topic_df.pkl", "wb" ) )
pickle.dump( lda_model, open( "model__lda_model.pkl", "wb" ) )
pickle.dump( count_vectorizer, open( "model__count_vectorizer.pkl", "wb" ) )
pickle.dump( stop_list, open( "model__stop_list.pkl", "wb" ) )
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 926 ms
Use useful data from RELAVENT_DATA.xlsx
x = dt3c.copy()
# Paper Name, Author Name(s), Year and Journal
print(x['date'].isna().sum())
x['date'] = x['date'].fillna(0)
print(x['date'].isna().sum())
doc_topic_df['year'] = x['date']
213 0 time: 19.6 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def combine_authors(author_list):
if isinstance(author_list, list):
return ', '.join(author_list)
return author_list
x['Author'] = x['Author'].fillna('-')
x['authors'] = x['Author'].apply(combine_authors)
doc_topic_df['authors'] = x['authors']
time: 13.7 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
print(x['title'].isna().sum())
x['title'] = x['title'].fillna('-')
print(x['title'].isna().sum())
doc_topic_df['title'] = x['title']
1646 0 time: 6.23 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def journal_name(text):
text = get_file_name(text)
t = re.findall('([0-9]+)_([a-zA-Z]+)', text, re.IGNORECASE)
return [i[1] for i in t][0]
doc_topic_df['journal'] = x['file_path'].apply(journal_name)
doc_topic_df['journal'].head(5)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
0 AH 1 AH 2 AH 3 AH 4 AH Name: journal, dtype: object
time: 66 ms
rt = pd.read_excel('/Users/farukcankaya/FC/GoogleDrive/DATA_RELEVANT.xlsx')
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/xlrd/xlsx.py:266: DeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/xlrd/xlsx.py:312: DeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/xlrd/xlsx.py:266: DeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
time: 19.5 s
rt.iloc[rt.index[rt['Filename'].str.match(get_file_name(dt['file_path'][0]))][0]]['Title']
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
'The effect of system characteristics, stage of adoption, and experience on institutional explanations for ERP systems choice'
time: 53.2 ms
titles = []
for i in range(0, len(dt3c['file_path'])):
if rt['Filename'].str.match(get_file_name(dt3c['file_path'][i])).any():
titles.append(rt.iloc[rt.index[rt['Filename'].str.match(get_file_name(dt3c['file_path'][i]))][0]]['Title'])
else:
titles.append(dt3c['title'][i])
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 3min 23s
pickle.dump( doc_topic_df, open( "model__doc_topic_df.pkl", "wb" ) )
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 1.09 s
# LOAD pre-trained model if you just want to make prediction here.
"""
norms = pickle.load( open( "model__norms.pkl", "rb" ) )
topic_array = pickle.load( open( "model__topic_array.pkl", "rb" ) )
df_topic_keywords = pickle.load( open( "model__df_topic_keywords.pkl", "rb" ) )
doc_topic_df = pickle.load( open( "model__doc_topic_df.pkl", "rb" ) )
lda_model = pickle.load( open( "model__lda_model.pkl", "rb" ) )
count_vectorizer = pickle.load( open( "model__count_vectorizer.pkl", "rb" ) )
stop_list = pickle.load( open( "model__stop_list.pkl", "rb" ) )
"""
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
'\nnorms = pickle.load( open( "model__norms.pkl", "rb" ) )\ntopic_array = pickle.load( open( "model__topic_array.pkl", "rb" ) )\ndf_topic_keywords = pickle.load( open( "model__df_topic_keywords.pkl", "rb" ) )\ndoc_topic_df = pickle.load( open( "model__doc_topic_df.pkl", "rb" ) )\nlda_model = pickle.load( open( "model__lda_model.pkl", "rb" ) )\ncount_vectorizer = pickle.load( open( "model__count_vectorizer.pkl", "rb" ) )\nstop_list = pickle.load( open( "model__stop_list.pkl", "rb" ) )\n'
time: 2.03 ms
def compute_dists(top_vec, topic_array):
'''
Returns cosine distances for top_vec compared to every article
'''
dots = np.matmul(topic_array, top_vec)
input_norm = np.linalg.norm(top_vec)
co_dists = dots / (input_norm * norms)
return co_dists
def produce_rec(top_vec, topic_array, doc_topic_df, rand = 15):
'''
Produces a recommendation based on cosine distance.
rand controls magnitude of randomness.
'''
# top_vec = top_vec + np.random.rand(30,)/(np.linalg.norm(top_vec)) * rand
co_dists = compute_dists(top_vec, topic_array)
return doc_topic_df.loc[np.argmax(co_dists)]
time: 693 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def sent_to_words(sentences):
for sentence in sentences:
yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))
# Define function to predict topic for a given new question.
def predict_topic(text, nlp=nlp):
global sent_to_words
# Clean
for i in range(len(text)):
text[i] = clean_text(text[i])
# Remove stopwords
for i in range(len(text)):
text[i] = remove_stopwords(text[i])
# Clean with simple_preprocess
mytext_2 = list(sent_to_words(text))
# Lemmatize is skipped !!!
# mytext_3_1 = []
# for sent in mytext_2:
# mytext_3_1.append(" ".join(sent))
# Lemmatize
mytext_3 = lemmatization(mytext_2, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])
#print('Text after lemmatization ({} characters):'.format(len(mytext_3[0])))
#print(mytext_3[0][0:100]+" ...")
# stemming is skipped !!!
#mytext_3_1 = []
#for t in mytext_3:
# mytext_3_1.append(stem_text(t))
# Vectorize transform
mytext_4 = count_vectorizer.transform(mytext_3)
#print('Count vectorizer of transformed text: {}'.format(mytext_4.shape))
#print('Vectorized data {}'.format(data_vectorized.shape))
#print('First 100 Feature names:')
#print(count_vectorizer.get_feature_names()[:100])
#print('')
# LDA Transform
topic_probability_scores = lda_model.transform(mytext_4)
topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), :].values.tolist()
return topic, topic_probability_scores
time: 2.52 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
# Predict the topic
TAR_6 = ["We examine how subjective performance evaluations are influenced by the level and controllability of an accompanying measure of a separate performance dimension. In our experiment, supervisors evaluate the office administration perfor- mance of a hypothetical subordinate. We find that supervisors’ subjective evaluations are directionally influenced by an accompanying objective measure of sales perfor- mance, even after excluding participants who perceive informativeness across measures. Consistent with concerns for fairness and motivation, we also find an asymmetric uncontrollability effect—supervisors’ evaluations are higher when an uncontrollable factor decreases the subordinate’s sales (i.e., they compensate for bad luck), but are not lower when the uncontrollable factor increases the subordinate’s sales (i.e., they do not punish for good luck). This evidence suggests that supervisors use discretion provided to evaluate performance on one task to adjust for perceived deficiencies in the evaluation of performance on other tasks. Our study integrates theories of cognitive bias and motivation, highlighting the need to consider the potentially interactive effects of different performance measures in multi-task settings I. INTRODUCTION Organizational incentive systems often allow managerial discretion in the evaluation of employee performance (Murphy and Oyer 2003). Subjective performance evaluation allows managers to use noncontractible information to assess actions and efforts that objective measures (such as those produced by the accounting system) are not able to capture, creating a more complete depiction of employee performance (Bol 2008).1 Subjectivity can therefore be useful in reducing risk to employees and improving the incentive alignment of the firm’s performance measurement system (Baker et al. 1994; Bushman et al. 1996; Hayes and Schaefer 2000).2 Many performance measurement systems include both objective measures and subjective evaluations (Prendergast 1999; Gibbs et al. 2004). While designed to capture separate dimensions of employee performance, these different measurement types can influence each other. For example, theories from psychology and organizational behavior suggest that subjective judgments can be unduly influenced by an individual’s knowledge of other, unrelated information (Nisbett et al. 1981; Bond et al. 2007). Such an influence could limit the complementary role of subjective evaluation in improving the overall informativeness of the measures used to evaluate performance. We examine how supervisors’ subjective performance evaluations are affected by the level and controllability of an objective measure of a separate aspect of performance. We analyze a two-dimensional employment setting in which an employee’s performance on one dimension (i.e., task) is measured objectively, while performance on the other dimension is evaluated subjectively by the supervisor. In many such settings, the objective performance measure is known by the supervisor before s/he subjectively evaluates the employee’s performance on the other dimension (Huber et al. 1987; Bommer et al. 1995). Our first research question examines whether the level of the objective measure has a directional impact on the supervisor’s subjective evaluation. Specifically, we examine whether, consistent with cognitive distortion, supervisors bias their subjective evaluations of performance on one dimension to be consistent with an objective measure of performance on a separate and unrelated dimension.3 Our second research question considers how such a spillover effect differs when the controllability of the objective performance measure is relatively low. Uncontrollability introduces noise and error into the performance measurement system (Feltham and Xie 1994), and has been shown to affect attribution judgments (Tan and Lipe 1997). Employees are likely to perceive uncontrollable performance measures to be unfair when the uncontrollability reduces measured performance. Perceptions of unfairness in compensation lead to reduced job satisfaction and motivation (Cohen-Charash and Spector 2001; Colquitt et al. 2001). We examine whether this expectation is reflected in supervisors’ use of their discretion, even when that discretion is provided for the evaluation of a separate task. Specifically, we examine whether supervisors use the discretion in their subjective evaluations to adjust for (or offset) the effects of an uncontrollable objective measure. Consistent with concerns for fairness and employee motivation, we predict that supervisors will make such an adjustment when the uncontrollable factor reduces measured performance (i.e., the employee suffers from bad luck) but not when it increases measured performance (i.e., the employee benefits from good luck). In our experiment, experienced supervisors employed by a large state university participate as evaluators in a hypothetical case setting. Participants assume the role of a regional director with supervisory authority over district managers who have both sales- and office administration-related duties. Objective individual sales information is given, after which participants are charged with subjectively evaluating the office administration performance of one district manager based on personal notes and staff interview responses, which are held constant in all conditions. The experiment employs a 2 3 2 þ 1 between-subjects design. In four treatment conditions, we manipulate the level of the manager’s individual sales at two levels by varying the objective sales score (high and low), and we manipulate the controllability of the individual sales measure at two levels by varying whether significant but uncontrollable events impacted the manager’s sales during the period. A fifth (control) condition includes only the information about the manager’s office administration performance. We find that supervisors’ subjective evaluations of the manager’s office administration performance are significantly higher (lower) when the objective level of the manager’s individual sales measure is relatively high (low). By excluding those participants who perceived the individual sales measure to be informative about the manager’s office administration performance in our primary analysis, we provide evidence of cognitive distortion of performance information as a result of exposure to the objective measure. We also find that the effect of the objective measure depends on its controllability. Specifically, participants use their subjective evaluations to adjust for the impact of uncontrollable events on the sales measure. Consistent with theory, this effect is asymmetric—participants’ evaluations are higher (relative to the high controllability condition) when the uncontrollable factor leads to an unfavorable outcome, but are no lower when the uncontrollable factor leads to a favorable outcome. Thus, we find that participants use their discretion to compensate for bad luck, but not to punish for good luck. This study contributes to the accounting and management literatures on performance evaluation. Prior research on bias in performance evaluation has found that supervisors’ subjective evaluations of current performance may be directionally influenced by prior performance information (Murphy et al. 1985; Huber et al. 1987; Kravitz and Balzer 1992) or information from different sources (Blakely 1993; Murphy and Cleveland 1995; Bono and Colbert 2005). Our study contributes to this line of research by showing that cognitive distortion can cause spillover from the objective evaluation of one dimension of performance to the subjective evaluation of performance on a separate and unrelated dimension. Prior research has also found evidence that controllability concerns can affect evaluators’ judgments in single-dimension performance-evaluation settings. For example, Tan and Lipe (1997) find that controllability moderates outcome effects in evaluators’ judgments (also see Brown and Solomon 1987). Our study reveals a very different controllability effect. Specifically, our results suggest that supervisors can use discretion that is provided for the evaluation of performance on a particular dimension to ‘‘correct’’ perceived deficiencies in other areas of the evaluation system. While the use of ‘‘borrowed’’ discretion can yield motivational benefits for employees, the intended benefits of that discretion (i.e., accurate reflection of performance on a specific dimension) can be reduced. Overall, our evidence indicates that the known level of an objective performance measure compromises the effectiveness of subjective performance evaluation for completing the picture of employee performance. Thus, our results have implications for the weight, timing, and nature of subjectivity in compensation contracting, suggesting that consideration of different measurement types in isolation can lead to incomplete conclusions about the optimal design of performance measurement systems. An asymmetric pattern of compensation payouts, in which managers are sheltered from downside risk but are allowed the advantages of upside risk, has been demonstrated in accounting"]
AOS_3 = ["This paper is focused on the way in which inter®rm relations can be drawn up and controlled. Drawing on con- tracting theories a model is built of the management control structure of inter®rm relationships. The model consists of three control patterns and of contingency factors that in ̄uence the choices between the patterns. Particular attention is paid to the role of trust. In building our model we take the perspective of the outsourcer. We try to re®ne our model by conducting case research in the ®eld of industrial renovation and industrial maintenance. In this ®eld we observed changes in the way outsourcing relationships are controlled. Case research can be very helpful for gaining insights into factors that in ̄uence the establishment of control structures of inter®rm relationships. By comparing the results of our case research we found institutional, strategic, cultural and historical factors to be of great importance for these control structures. 5 1999 Elsevier Science Ltd. All rights reserved. The boundaries of ®rms are continuously sub- ject to changes. Firms add new activities, hive o existing activities and set up new forms of coop- eration with other ®rms. The concentration on core activities is a tendency that already started several years ago. Non-core activities are hived o, so that the ®rm can concentrate on the activ- ities which it can perform well and which can thus produce the highest added value. Insight into which activities belong to the non-core activities is subject to changes too. We observe that conglom- erates with a great variety of products hive o complete product lines, but we also ®nd that ®rms, although they do not change their product mix, leave the production of components to other ®rms. We can also observe that ®rms outsource the provision of supporting services even more. Even services which are essential for the continuity of the ®rm, such as administration, information technology and maintenance. When the out- sourcing concerns essential components and ser- vices we observe the setting up of close forms of cooperation between ®rms. Hiving o essential components and services requires a speci®c embeddedness of the outsourcing relation in order to guarantee its continuity and to retain the eco- nomic advantages of outsourcing in the long run. This makes demands on the macro as well as micro level. On the macro level these develop- ments must be supported by legal and institutional frameworks (such as contract law and the attitude of branch organisations and the trade unions). On the micro level they make demands on the orga- nisation of the ®rm's own activities, the coordina- tion with the suppliers and the employees' willingness to work closely together with people from outside their own ®rm. The structuring and control of inter®rm relations require the establish- ment of suitable management control systems and processes. This study is in particular aimed at the demands outsourcing relations make on the micro level. The question is: how can ®rms which enter into strategic alliances with other ®rms structure the management control of such inter®rm relationships? Management control and management accounting research usually concentrates on rela- tions within organisations with varying degrees of decentralisation of decision making. Robert Anthony's (1965, 1988) well-known framework in which the decomposition of the organisation into responsibility centers is paramount, is the implicit or explicit point of departure for a lot of formal behavioural and contingency research in manage- ment control. In numerous organisations a hor- izontalisation of management control relations can be observed. These control relations are not hierarchically based but are relations between equal internal parties. Customer-supplier relation systems are being set up with forms of internal contracting (service level agreements), e.g. with respect to facility services (Van der Meer-Kooistra, 1994; Vosselman, 1996). There used to be captive buyers and sellers, but now it is no longer excep- tional for departments to be allowed to deliver or to buy on external markets within rather ̄exible limits. Otley (1994) too observes such a horizontalisation and proposes a broader management control fra- mework. Research into horizontal management control relations should not remain restricted to intra®rm relations, but be extended to inter®rm relations. Recently Hopwood urged ``(the) exam- ination of some of the accounting and informational consequences of more explicit concerns with the management of the supply chain and a more con- scious questioning of what activities reside within and without the enterprise'' (Hopwood, 1996, p. 590). In the relevant literature little attention is given to management control issues with regard to forms of cooperation between independent com- panies (inter®rm relations). It is in particular the strategic cost management literature (Cooper, 1995; Shank & Govindarajan, 1993) which draws attention to the importance of looking beyond the boundaries of the ®rm. These publications emphasise the value chain approach, which not only focuses on the relations between the various subsequent activities within the ®rm but also on the ®rm's relations with the suppliers of raw materials, components etc. and the buyers. Berry (1995) also focuses on extra-organisational control problems, in particular control in network orga- nisations. According to Gietzmann (1996) the role of the management accountant is changing due to control beyond the boundaries of the concern. When deciding on what should or should not be contracted out this role is much more complex than just calculating the production costs and comparing them with the market price. Make or buy decisions are becoming more and more of a strategic nature (see e.g. Tayles & Drury, 1997). ``The management accountant should play a cri- tical support role in the strategy debate concerning which elements (subcomponents) of product development can be outsourced, which should be developed in house and which do not warrant development since o the shelf subcomponents suce'' (Gietzmann, p. 624). Therefore the man- agement accountant needs not only information about the production costs and market prices but he also needs information about the costs and bene®ts of the speci®c forms of governance connected with these choices. Nowadays many make or buy decisions have evolved into what could be called `extended make or buy' decisions. An extended make or buy deci- sion can be divided into three separate, but inter- related elements. Firstly, the decision to purchase goods or services on a structural basis from third parties or to produce the goods or services in- house. Secondly, in the case of purchasing from third parties, the decision regarding the party or parties contracted. The nature of the party or parties in ̄uences the chance, and extent, of relational risks. Thirdly, decisions regarding the design of the internal management control system or the design of the outsourcing relationship."]
topic, prob_scores = predict_topic(text = TAR_6)
print('topic')
print(topic)
print('')
print('prob_scores')
print(prob_scores[0])
print("shape of prob_scores: {}".format(prob_scores[0].shape))
print('')
print("Try to make recommendation. Inputs:")
print("- prob_scores")
print("- topic_array: {} prob. distribution of topics for each document that is calculated by LDA".format(topic_array.shape))
print("- doc_topic_df: {} that is actually output of the LDA but combined with file_path, etc.".format(doc_topic_df.shape))
print('')
produce_rec(prob_scores.flatten(), topic_array, doc_topic_df)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
topic ['performance', 'manager', 'incentive', 'compensation', 'measure', 'contract', 'pay', 'effort', 'base', 'bonus', 'executive', 'increase', 'ceo', 'managerial', 'agent', 'principal', 'period', 'effect', 'ceos', 'level'] prob_scores [6.43358305e-02 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.52475979e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 4.54861103e-03 2.68024658e-05 3.32710392e-01 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 1.58546336e-02 2.68024658e-05 2.68024658e-05 2.72726737e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 3.08147624e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 6.31989332e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.52934069e-01 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 3.13354920e-01 2.68024658e-05 2.68024658e-05 2.68024658e-05] shape of prob_scores: (70,) Try to make recommendation. Inputs: - prob_scores - topic_array: (15189, 70) prob. distribution of topics for each document that is calculated by LDA - doc_topic_df: (15189, 76) that is actually output of the LDA but combined with file_path, etc.
file_path /Users/farukcankaya/Google Drive/AllPapers/1_TAR/6_TAR.pdf content_10 effect subjective performance evaluation controllability subjective performance evaluation influence level controllability accompany measure separate performance dimension experiment supervisor evaluate subordinate supervisor subjective evaluation directionally influence accompany objective measure exclude participant perceive informativeness measure consistent concern uncontrollability effectsupervisor evaluation higher uncontrollable factor decrease subordinate compensate bad uncontrollable factor increase subordinate punish evidence suggest supervisor discretion provide performance task perceive deficiency evaluation performance task study integrate theory cognitive motivation highlight potentially interactive effect performance measure multi task setting keyword subjective performance evaluation availability contact author helpful comment suggestion midyear meeting birkey valuable research assistance research fund editor note accept submit accept publish online introduction rganizational incentive system managerial discretion evaluation employee performance oyer subjective performance evaluation manager noncontractible information assess action effort objective measure produce accounting capture create complete depiction employee performance subjectivity reduce risk employee improve incentive alignment firm performance system include objective measure subjective evaluation design capture separate dimension employee performance measurement type influence theory psychology organizational behavior suggest subjective judgment unduly influence individual knowledge unrelated information limit complementary role subjective evaluation improve informativeness measure evaluate performance supervisor subjective performance evaluation affect level controllability objective measure analyze dimensional employment set employee performance dimension task measure objectively performance dimension evaluate subjectively supervisor setting objective performance measure supervisor subjectively evaluate employee performance dimension research question examine level objective measure directional impact supervisor subjective evaluation specifically examine consistent cognitive distortion supervisor subjective evaluation performance dimension consistent objective measure performance separate unrelated dimension research question consider effect differ objective performance measure uncontrollability introduce noise attribution judgment lipe employee perceive uncontrollable performance measure unfair uncontrollability reduce measure performance perception reduce job satisfaction motivation spector expectation reflect supervisor discretion discretion provide evaluation separate task specifically examine supervisor discretion subjective evaluation adjust offset effect uncontrollable objective measure consistent concern fairness employee motivation predict define subjectivity judgment base personal impression opinion verifiable court noncontractible subjectivity performance evaluation introduce subjective performance measure allow flexibility weight objective performance measure allow discretional adjustment base factor performance measure ante mechanism supervisor subjective evaluation influence subjective measure objective measure specifically examine effect objective performance measure measure subjective evaluation perform interactive effect controllability feature objective performance measure accounting review supervisor adjustment uncontrollable factor reduce measure performance employee suffer bad increase measure performance employee benefit hypothetical setting participant assume role regional director supervisory authority administration duty objective individual information participant charge subjectively evaluate performance manager base personal note staff interview response experiment employ subject design treatment condition manipulate level manager individual level vary objective score manipulate controllability individual measure level vary significant uncontrollable event impact manager period control condition include information performance supervisor subjective evaluation performance significantly high objective level manager individual measure exclude participant perceive individual measure informative performance primary analysis provide evidence cognitive distortion performance information result objective measure effect objective measure depend controllability specifically participant subjective evaluation adjust impact measure consistent theory effect asymmetricparticipant evaluation high relative controllability condition uncontrollable factor lead unfavorable outcome uncontrollable factor lead favorable outcome participant discretion compensate bad punish study contribute accounting management literature performance evaluation research performance evaluation supervisor subjective evaluation current performance directionally influence performance information information source study contribute line research show cognitive distortion spillover objective evaluation dimension performance subjective evaluation performance separate unrelated dimension research evidence controllability concern affect evaluator judgment single dimension performance evaluation setting controllability moderate reveal controllability effect specifically result suggest supervisor discretion provide evaluation performance dimension area evaluation borrow discretion yield motivational benefit employee intend benefit discretion accurate reflection performance specific dimension reduce evidence level objective performance measure compromise effectiveness subjective performance evaluation complete picture employee performance result implication weight timing nature subjectivity compensation contracting suggest consideration measurement type isolation lead incomplete conclusion design performance measurement pattern compensation payout manager shelter downside risk allow advantage upside risk demonstrate accounting spillover effect subjective performance evaluation accounting review research show similar pattern differ research demonstrate phenomenon borrow discretion illustrate effect performance evaluation set explanation personal relationship managerial entrenchment issue corporate governance study demonstrate feature account information controllability objective performance measure produce difference spillover effect induce decision maker judgment predominantly effect objective performance measure drive directional content spillover effect take form assimilation objective performance measure reduce uncontrollable factor fairness motivational concern trigger induce corrective adjustment offset directional study integrate theory cognitive distortion fairness provide evidence objective subjective performance measure depend fundamental quality objective measure section present background hypothesis section describe experimental methodology section present result section conclude background hypothesis research explore potential drawback subjective performance evaluation study role human judgment inability verify outcome rise number problem limit informativeness incentive strengthen role subjective measure topel area focus supervisor personal incentive distort evaluation find leniency evaluation incentive factor executive incentive consistent goal high raise subordinate improved motivation encouragement signal poor performer accuracy evaluation literature examine subjective performance evaluation influence cognitive limitation research context effect examine assimilation contrast arise knowledge employee performance period evaluate current period performance halo effect find supervisor attend general impression evaluate subordinate nisbett research specific accounting domain explore role cognitive limitation performance evaluation weight performance measure underweight unique measure favor common measure ittner provide evidence supervisor great weight outcome result measure input measure weight objective quantitative measure manager reliance anchor adjustment heuristic prevent incorporation contract subjective information allocation bonus study suggest cognitive limitation preclude benefit introduce subjectivity compensation contract study examine potentially interactive effect objective subjective performance measure specifically examine cognitive limitation preclude independent assessment separate review unrelated performance dimension controllability objective measure influence interaction hypothesis development task set employee performance task evaluate objectively performance separate task evaluate subjectively setting objective performance measure period subjective performance evaluation take supervisor production performance period conduct formal evaluation employee performance qualitative dimension leadership teamwork creativity communication attitude subjective performance evaluation verify party supervisor influence preference evaluate performance topel argue absence personal incentive evaluation supervisor influence observed level performance task subjectively evaluate employee performance separate task argue form objective measure spillover depend controllability degree employee affect measure alter action input antle cognitive distortion supervisor knowledge employee objective performance dimension affect subjective evaluation performance separate dimension induce cognitive information distortion specifically supervisor unintentionally subjective evaluation consistent level performance objective measure research show individual tend unknowingly process information previously belief desire outcome develop preference argue evaluation single option information observe evoke evaluative disposition subjective encode weighting interpretation subsequent information influence coherent disposition multi task performance evaluation occur level performance dimension supervisor unknowingly process information performance separate dimension direction supervisor observe level performance dimension interpret information subordinate performance separate dimension favorably supervisor observe level performance dimension assume supervisor allow form discretion subjective weighting subjective adjustment objective measure word discretion reside supervisor evaluation performance task cognitive distortion conceptually context effect anchoring adjustment confirmation emerge consensus anchor adjustment heuristic arise biased accessibility information mussweiler confirmation reflect information study relate subjective interpretation information obtain cognitive distortion processing phenomenon fitting explanation mechanism underlie directional predict spillover effect subjective performance evaluation accounting review mention objective measure performance supervisor conduct subjective evaluation subordinate performance literature cite suggest knowledge generate directional affect subsequent judgment predict consistent cognitive distortion supervisor subjective performance evaluation influence direction subordinate objective performance level supervisor subjective performance evaluation high employee performance unrelated objective measure higher important note objective level performance task supervisor informative employee performance task supervisor intentionally incorporate information objective measure subjective evaluation economic base model performance measurement suggest costless measure incremental information employee performance include evaluation datar reason intuitively extended information measure performance subjective evaluation performance task study spillover effect arise cognitive process independent supervisor perception informativeness experiment create set performance measure unrelated analysis exclude participant perceive informativeness measure order provide conservative test theory controllability objective measure influence spillover effect induce controllable performance measure prefer contracting perspective uncontrollability reduce informativeness measure employee action effort agent compensate additional risk uncontrollable factor practice measure perfectly controllable extremely rare noise common feature objective performance measure primary reason subjective performance evaluation objective measure controllability spillover effect form predict uncontrollability objective performance measure trigger supervisor consideration measure quality effect employee evaluation specifically supervisor incentive fairness performance evaluation simply individual preference fehr fischbacher importantly employee perception unfairness evaluation compensation supervisor adjust performance target address fairness concern arise compensation contract importance employee satisfaction motivation productivity supervisor discretion correct adjust uncontrollable effort create orthogonal performance dimension unable eliminate conscious influence base perceive partial explanation result setting task bundle single employee presume complementarity performance task milgrom difficult complete unrelatedness multi task experimental setting design realistic review provide separate purpose adjustment uncontrollable factor depend directional impact uncontrollable factor measure performance employee feel measure capture effort contribution firm result compensation result measure unfair suffer reduced satisfaction motivation order protect employee downside risk avoid negative effect employee motivation supervisor exercise discretion subjective evaluation adjust upward give high evaluation countervail level uncontrollable measure supervisor subjective evaluation bias downward exposure objective measure concern employee motivation offset lead supervisor adjust upward ensure evaluation employee perceive contrast uncontrollable factor measure performance high employee perceive unfairness feel reduced motivation supervisor discretion correct discrepancy measure actual performance give evaluation consider inappropriate unfair departure prescribe evaluation procedure supervisor recognize measure fail capture employee incentive correct directional predict expect summary predict supervisor discretion adjust uncontrollability separate performance measure asymmetric pattern uncontrollable factor lead unfavorable outcome supervisor consideration employee fairness perception motivation offset directional predict high evaluation relative controllability condition uncontrollable factor lead favorable outcome supervisor discretion correct predict difference subjective evaluation relative controllability condition employee performance unrelated objective measure supervisor subjective performance evaluation high objective measure reduce uncontrollable factor hypothesis employee performance unrelated objective measure supervisor subjective performance evaluation affect objective measure increase uncontrollable factor method participant complete performance evaluation task hypothetical setting assume role regional director industrial pipe fit company regional director job oversee number district manager responsible individual office task perform subjective performance district manager manager score objective individual measure consistent established principle unfairness feel acutely distribute reward feel similarly research organizational behavior find consideration fairness evaluation procedure trigger outcome effect subjective performance evaluation accounting review participant public participate experiment participant target university database search academic employee supervisory position receive letter mail invite complete study participant name enter random drawing participant report average year total work experience year supervisory experience current position participant report average year performance evaluation experience percent report formal informal performance evaluation duty average subordinate employee current position finally participant report percent performance evaluation duty involve subjective evaluation objective measurement experimental design experiment employ design manipulate district manager score measure level manipulate controllability objective measure level add control condition objective information provide independent variable treatment condition objective measure performance evaluation district manager individual recent period measure score scale score connect level target district manager performance dimension manipulate vary individual score specifically district manager individual score state condition controllability manipulate varying degree factor influence manager individual controllability condition participant tell market condition stable period unforeseen uncontrollable event impact manager controllability condition participant tell uncontrollable eventspecifically open measure close measure facility customer manager district impact manager period dependent variable primary dependent variable subjective evaluation administration performance order participant evaluation information include collection personal note interview response office staff material condition read information participant provide performance evaluation score scale reflect assessment district performance answer number experimental question include participant gender year work supervisor experience number evaluate subordinate statistical analysis result variable interact effect procedure participant complete online experiment convenience enter url web browser survey program randomly assign participant condition treatment condition control condition information ask participant assume hypothetical situation work regional director industrial pipe fitting company compress organizational show regional director supervisory authority district manager turn supervised office staff district manager responsibilitie performance measure inform participant regional director duty conduct semiannual performance evaluation performance evaluation comprise component objective score base individual subjective evaluation performance instruction note component independent job function capture independent performance job function unrelated performance component score scale average equally weighted evaluation score participant tell task conduct performance evaluation district manager objective measure table show district manager individual score scale provide target district manager manipulate manipulate controllability information point subjective evaluation participant tell personal note district manager period provide addition participant tell unable observe performance basis conduct interview office staff interview response provide personal note interview response participant design portray average administration performance condition participant ask provide subjective office administration evaluation individual information read information participant ask score scale reflect assessment district performance ask average performance evaluation component arrive evaluation assess confidence judgment ask number result manipulation check information sufficiency include manipulation check question experimental questionnaire participant recall target manager individual score high condition condition participant judge controllability measure high controllability condition controllability condition manipulation level controllability manager individual measure successful ask participant rate point scale agreement statement personal note interview response target manager allow reasonable judgment performance measure significantly high scale midpoint spillover effect subjective performance evaluation accounting review ask participant rate point scale confidence feel evaluation judgment measure significantly high scale midpoint result suggest participant feel sufficient information performance reasonable assessment table show standard performance score experimental condition figure display controllability condition level control condition figure show expect pattern result evaluation high condition control condition condition predict asymmetric pattern treatment apparent controllability condition preliminary analysis untabulate control condition significantly condition significantly high condition result confirm control condition extreme floor ceiling effect prevent hypothesize result reveal directional spillover effect occur direction hypothesis test cognitive distortion predict participant subjective office administration evaluation high individual measure high order conduct clean conservative test theory adjustment predict controllability work effect level test compare condition controllability table descriptive statistic square std error evaluation score measure level controllability controllability controllability row level manipulate vary target manager achieve score period score controllability manipulate vary event manager control significantly affected period control objective performance information provide review Topic 1 0.0977721 Topic 2 6.74172e-06 Topic 3 0.0236816 Topic 4 6.74172e-06 Topic 5 6.74172e-06 Topic 6 6.74172e-06 Topic 7 0.000693412 Topic 8 6.74172e-06 Topic 9 6.74172e-06 Topic 10 6.74172e-06 Topic 11 6.74172e-06 Topic 12 6.74172e-06 Topic 13 6.74172e-06 Topic 14 6.74172e-06 Topic 15 6.74172e-06 Topic 16 6.74172e-06 Topic 17 6.74172e-06 Topic 18 6.74172e-06 Topic 19 6.74172e-06 Topic 20 6.74172e-06 Topic 21 6.74172e-06 Topic 22 0.00235869 Topic 23 0.00728934 Topic 24 6.74172e-06 Topic 25 0.269144 Topic 26 6.74172e-06 Topic 27 0.0151219 Topic 28 6.74172e-06 Topic 29 6.74172e-06 Topic 30 6.74172e-06 Topic 31 0.0174398 Topic 32 6.74172e-06 Topic 33 6.74172e-06 Topic 34 0.004748 Topic 35 6.74172e-06 Topic 36 6.74172e-06 Topic 37 0.00888905 Topic 38 6.74172e-06 Topic 39 0.00157905 Topic 40 6.74172e-06 Topic 41 0.00179846 Topic 42 6.74172e-06 Topic 43 6.74172e-06 Topic 44 6.74172e-06 Topic 45 6.74172e-06 Topic 46 0.00206575 Topic 47 6.74172e-06 Topic 48 6.74172e-06 Topic 49 6.74172e-06 Topic 50 0.0126454 Topic 51 6.74172e-06 Topic 52 6.74172e-06 Topic 53 6.74172e-06 Topic 54 6.74172e-06 Topic 55 6.74172e-06 Topic 56 6.74172e-06 Topic 57 6.74172e-06 Topic 58 6.74172e-06 Topic 59 6.74172e-06 Topic 60 6.74172e-06 Topic 61 0.263987 Topic 62 0.00671104 Topic 63 6.74172e-06 Topic 64 6.74172e-06 Topic 65 6.74172e-06 Topic 66 6.74172e-06 Topic 67 0.263718 Topic 68 6.74172e-06 Topic 69 6.74172e-06 Topic 70 6.74172e-06 year 2019-04-03T19:47:28Z authors Jasmijn C.Bol and Steven D.Smith title Spillover Effects in Subjective Performance Evaluation: Bias and the Asymmetric Influence of Controllability journal TAR Name: 7631, dtype: object
time: 455 ms
from sklearn.metrics.pairwise import cosine_similarity
def produce_rec_top_n(top_vec, topic_array, doc_topic_df, n=5):
co_dists = compute_dists(top_vec, topic_array)
index_top_n = np.argpartition(co_dists, -n)[-n:]
index_top_n = index_top_n[np.argsort(-co_dists[index_top_n])]
return doc_topic_df.loc[index_top_n]
topic, prob_scores = predict_topic(text = TAR_6)
topic_columns = ['Topic '+str(i) for i in range(1,df_topic_keywords.shape[0]+1)]
recs = produce_rec_top_n(prob_scores.flatten(), topic_array, doc_topic_df)
rec_top_1 = recs[topic_columns].head(1).values
print('rec_top_1:')
print(rec_top_1)
print('prob_scores:')
print(prob_scores.flatten())
print('cosine_similarity:')
print(cosine_similarity(rec_top_1,prob_scores))
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
rec_top_1: [[9.77720683e-02 6.74172453e-06 2.36816121e-02 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.93412210e-04 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 2.35869064e-03 7.28934337e-03 6.74172453e-06 2.69144331e-01 6.74172453e-06 1.51218947e-02 6.74172453e-06 6.74172453e-06 6.74172453e-06 1.74398172e-02 6.74172453e-06 6.74172453e-06 4.74799975e-03 6.74172453e-06 6.74172453e-06 8.88905297e-03 6.74172453e-06 1.57904554e-03 6.74172453e-06 1.79846268e-03 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 2.06574704e-03 6.74172453e-06 6.74172453e-06 6.74172453e-06 1.26454285e-02 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 2.63986647e-01 6.71104307e-03 6.74172453e-06 6.74172453e-06 6.74172453e-06 6.74172453e-06 2.63718093e-01 6.74172453e-06 6.74172453e-06 6.74172453e-06]] prob_scores: [6.43358305e-02 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.52475979e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 4.54861103e-03 2.68024658e-05 3.32710392e-01 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 1.58546336e-02 2.68024658e-05 2.68024658e-05 2.72726737e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 3.08147624e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 6.31989332e-03 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.52934069e-01 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 2.68024658e-05 3.13354920e-01 2.68024658e-05 2.68024658e-05 2.68024658e-05] cosine_similarity: [[0.98738299]] time: 245 ms
recs['file_path']
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
7631 /Users/farukcankaya/Google Drive/AllPapers/1_TAR/6_TAR.pdf 11184 /Users/farukcankaya/Google Drive/AllPapers/4_AOS/898_AOS.pdf 850 /Users/farukcankaya/Google Drive/AllPapers/11_BAR/433_BAR.pdf 7247 /Users/farukcankaya/Google Drive/AllPapers/1_TAR/158_TAR.pdf 2494 /Users/farukcankaya/Google Drive/AllPapers/3_JAR/215_JAR.pdf Name: file_path, dtype: object
time: 4.53 ms
def accuracy_n(file_path, data, top_n=[1]):
topic, prob_scores = predict_topic([data])
topic_columns = ['Topic '+str(i) for i in range(1,df_topic_keywords.shape[0]+1)]
recs = produce_rec_top_n(prob_scores.flatten(), topic_array, doc_topic_df, max(top_n))
existence = []
for n in top_n:
if recs['file_path'].head(n).str.contains(get_file_name(file_path)).any():
existence.append(1)
else:
existence.append(0)
return pd.Series(existence)
time: 802 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
## PARALLEL TEST
xx = dt.copy().drop(unknown_lang_indexes_content_3)
xx = xx.drop(unknown_lang_indexes_content_10)
top_n = [1, 5, 10, 20, 50, 100]
col_acc_n = ['acc_top_'+str(n) for n in top_n]
xx[col_acc_n] = xx.parallel_apply(lambda x: accuracy_n(x['file_path'], x['page_1'], top_n), axis=1)
xx.info()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1
<class 'pandas.core.frame.DataFrame'> Int64Index: 15750 entries, 0 to 15750 Columns: 2445 entries, file_path to acc_top_100 dtypes: float64(330), int64(337), object(1778) memory usage: 293.9+ MB time: 9min 20s
for n in top_n:
print('Top-'+str(n)+' Score:' + str(xx['acc_top_'+str(n)].sum()/xx['acc_top_'+str(n)].count()))
Top-1 Score:0.3417142857142857 Top-5 Score:0.5310476190476191 Top-10 Score:0.6081269841269842 Top-20 Score:0.6821587301587302 Top-50 Score:0.7671111111111111 Top-100 Score:0.8276190476190476 time: 14.2 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.